Yes, the dotplot worked for me with the sample data!

Yet, I can also not reproduce your error... though I noticed your object gse was not generated according to the code you posted! Because nPerm slot of gse is not empty...! #...nPerm 1e+05 whereas it should be empty when specifying the argument by = "fgsea" (which is actually the default setting) .

Anyway, below find some code + output of enrichGO and gseGO for Arabidsopsis. You should be able tor run everything yourselves as well, and modify settings according to your dataset and needs.

> # load required libraries
> library(clusterProfiler)
> library(org.At.tair.db)
> 
> # make up some data
> # extract all 27,416 TAIR ids from org.At.tair.db
> genes.all <- keys(org.At.tair.db)
> length(genes.all)
[1] 27416
> 
> # randomly select 1750 genes
> # these 1750 genes represent significantly regulated genes
> genes.selected <- base::sample(genes.all, 1750)
> 
> 
> # to get started, perform Gene Ontology ORA analysis
> # note that no significance cutoffs are applied (i.e Cutoff=1)
> # because I use random ids as input but still woud like to obtain results!
> # for GSEA see below
> results.GOBP.ora <- enrichGO(
+   gene=genes.selected,
+   OrgDb="org.At.tair.db",
+   keyType = "TAIR",
+   ont = "BP",
+   pvalueCutoff = 1,
+   pAdjustMethod = "none",
+   universe=genes.all,
+   qvalueCutoff = 1,
+   minGSSize = 10,
+   maxGSSize = 500,
+   readable = TRUE,
+   pool = FALSE)
> 
> #check
> as.data.frame(results.GOBP.ora)[1:4,]
                   ID
GO:0045292 GO:0045292
GO:1901606 GO:1901606
GO:0048646 GO:0048646
GO:0009065 GO:0009065
                                                        Description
GO:0045292                       mRNA cis splicing, via spliceosome
GO:1901606                       alpha-amino acid catabolic process
GO:0048646 anatomical structure formation involved in morphogenesis
GO:0009065            glutamine family amino acid catabolic process
           GeneRatio   BgRatio      pvalue    p.adjust    qvalue
GO:0045292    7/1662  31/26032 0.002889030 0.002889030 0.9764508
GO:1901606   13/1662  87/26032 0.003420239 0.003420239 0.9764508
GO:0048646   30/1662 278/26032 0.003501675 0.003501675 0.9764508
GO:0009065    5/1662  18/26032 0.004481046 0.004481046 0.9764508
                                                                                                                                                                                          geneID
GO:0045292                                                                                                                                                          NA/AT/AtELF5/NA/PRP4KA/NA/NA
GO:1901606                                                                                                             ALDH5F1/NA/ARGAH1/AtTAT1/PDH2/LIP1/LKR/GAD2/THA2/ISS1/CHY1/AGT2/DELTA-OAT
GO:0048646 TMKL1/AMT1;1/ANQ1/QRT3/PAPS1/AtMYB88/AJH1/ATLOX5/CuAOζ-zeta/AHL16/AtPLAIVB/HEC3/ALE2/CSI1/LACS2/BOP2/NA/ATMPK13/NA/ATHB-2/NA/GAT/AtMYB117/NA/CYP703/MEE48/LRS1/TCP3/ATBRM/CENP-C
GO:0009065                                                                                                                                                    ALDH5F1/ARGAH1/PDH2/GAD2/DELTA-OAT
           Count
GO:0045292     7
GO:1901606    13
GO:0048646    30
GO:0009065     5
> 
> # generate plots
> library(enrichplot)  #needed for function "pairwise_termsim"
> 
> barplot(results.GOBP.ora, showCategory=10)
> dotplot(results.GOBP.ora, showCategory=10)
> cnetplot(results.GOBP.ora, showCategory=10)
> heatplot(results.GOBP.ora, showCategory=10)
> emapplot( pairwise_termsim(results.GOBP.ora), node_scale=1.5,layout="kk" )
> 
>
>
>
> # now GSEA with GO categories
> # for GSEA a per-gene metric is required that is used for ranking.
> # assume dataset consists of the 27.4k TAIR ids.
> # make up fold changes (= ranking metric) for the TAIR ids ranging from -15 to 15.
> sorted_dfx <- runif(n = length(genes.all), min = -15, max = 15)
> names(sorted_dfx) <- genes.all 
> # for GSEA, sort on FC
> sorted_dfx <- sort(sorted_dfx, decreasing = TRUE)
> 
> #check
> head(sorted_dfx)
AT2G38740 AT2G35030 AT1G03060 AT3G21120 AT4G11290 AT5G12420 
 14.99932  14.99798  14.99436  14.99171  14.99072  14.98952 
> 
> # run GSEA with GO categories
> # again, no significant cutoff is used!
> results.GOALL.gsea <- gseGO(geneList=sorted_dfx, 
+   ont ="ALL", 
+   keyType = "TAIR", 
+   minGSSize = 3, 
+   maxGSSize = 800, 
+   pvalueCutoff = 1, 
+   verbose = TRUE, 
+   OrgDb = "org.At.tair.db",
+   by = "fgsea",
+   pAdjustMethod = "none")
preparing geneSet collections...
GSEA analysis...
leading edge analysis...
done...
> 
> # convert TAIR ids into Symbols
> results.GOALL.gsea <- setReadable(results.GOALL.gsea, "org.At.tair.db", "TAIR")
> 
> #check
> as.data.frame(results.GOALL.gsea)[1:4,]
           ONTOLOGY         ID
GO:0033962       BP GO:0033962
GO:0015086       MF GO:0015086
GO:0009979       MF GO:0009979
GO:0080102       MF GO:0080102
                                                     Description setSize
GO:0033962                                       P-body assembly      15
GO:0015086        cadmium ion transmembrane transporter activity      13
GO:0009979 16:0 monogalactosyldiacylglycerol desaturase activity       9
GO:0080102 3-methylthiopropyl glucosinolate S-oxygenase activity       3
           enrichmentScore       NES       pvalue     p.adjust   qvalues
GO:0033962       0.6494014  2.104057 0.0002644794 0.0002644794 0.9099991
GO:0015086       0.6615598  2.041589 0.0009006453 0.0009006453 0.9099991
GO:0009979      -0.7234794 -2.006594 0.0014432835 0.0014432835 0.9099991
GO:0080102      -0.9365204 -1.688523 0.0015708730 0.0015708730 0.9099991
           rank                   leading_edge
GO:0033962 4449 tags=53%, list=16%, signal=45%
GO:0015086 3331 tags=46%, list=12%, signal=41%
GO:0009979 7104 tags=78%, list=26%, signal=58%
GO:0080102  682 tags=100%, list=2%, signal=98%
                                           core_enrichment
GO:0033962  BCHA1/NA/LSM3B/AtPAT1H2/PAT1/emb1644/NA/DCP5-L
GO:0015086 ATNRAMP5/ATNRAMP2/ATNRAMP1/ATHMA2/ATHMA4/ATEIN2
GO:0009979                  NA/ADS2/NA/NA/ADS3/ADS1/ADS1.2
GO:0080102                                         FMO/FMO
> 
> 
> #generate plots
> library(ggplot2)  #needed for function "facet_grid"
> # The plot that gave an error with you:
> dotplot(results.GOALL.gsea, showCategory=10, split=".sign", font.size = 8) + facet_grid(.~.sign)
> 
> # other plots also work!
> cnetplot(results.GOALL.gsea, foldChange=sorted_dfx, showCategory=10,
+     cex_label_category = 0.8, cex_label_gene = 0.6)
> heatplot(results.GOALL.gsea, foldChange=sorted_dfx, showCategory=10)
> emapplot( pairwise_termsim(results.GOALL.gsea), node_scale=1.5,layout="kk" )
> 
>
> # Lastly, based on your code snippets you also tried to perform Disease Ontology (DO)
> # enrichment analysis. Since the diseases in DO are all human (and not plant!),
> # this does noty give any results, hence an error is reported! :-)
> # This is a different one than you reported, though...
> library(DOSE)
> edo2dot <- gseDO(sorted_dfx)
preparing geneSet collections...
--> Expected input gene ID: 3856,4316,8419,7422,5445,56717
Error in check_gene_id(geneList, geneSets) : 
  --> No gene can be mapped....
>

As example, the dotplot you tried to generate:

Also the cnetplot from the GSEA run: