I have the below data:

Samples        Genotype  treatment Time
D2_WT_mock1     WT       mock      D2
D2_WT_mock2     WT       mock      D2 
D2_WT_mock3     WT       mock      D2
D2_KO_mock1     KO       mock      D2
D2_KO_mock2     KO       mock      D2
D2_KO_mock3     KO       mock      D2
D2_WT_inf1      WT       inf       D2
D2_WT_inf2      WT       inf       D2
D2_WT_inf3      WT       inf       D2
D2_KO_inf1      KO       inf       D2
D2_KO_inf2      KO       inf       D2
D2_KO_inf3      KO       inf       D2

D6_WT_mock1     WT       mock      D6
D6_WT_mock2     WT       mock      D6
D6_WT_mock3     WT       mock      D6
D6_KO_mock1     KO       mock      D6
D6_KO_mock2     KO       mock      D6
D6_KO_mock3     KO       mock      D6
D2_WT_inf1      WT       inf       D6
D6_WT_inf2      WT       inf       D6
D6_WT_inf3      WT       inf       D6
D6_KO_inf1      KO       inf       D6
D6_KO_inf2      KO       inf       D6
D6_KO_inf3      KO       inf       D6

I have three different conditions, and would like to look at different comparisons for differential expressed genes. I combined the factors for easier interpretation. However when I look at the PCA plot (see attached), I do see a lot of variation within D6.

https://ibb.co/TvnWRS3

My goal is to see differences between genotypes based on treatment (using interaction terms), and main effects as well (for D2 and D6).

So my question is in this multi factor comparison, should I Run DEseq2 with all samples and remove the D6 ones by droplevels or run DEseq2 on D2 and D6 samples separately and make interpretations. Thanks.

If deseq2 is done together I could probably do comparisons between D2 and D6, if not that is alright if I just do these two separate.

I would tend to run the models over the entire dataset. Some of the variation seen among D6 will be taken care of by the treatment and genotype coefficients.