Hello, I am not sure I am building the proper design formula for the question I want to answer

I have the following samples with three factors; clone, the structure and the condition.

clone structure diabetic
1 07        2D      Dia
2 21        2D     Ctrl
3 23        2D      Dia
4 32        2D     Ctrl
5 34        2D      Dia
6 43        2D     Ctrl
7 07        3D      Dia
8 21        3D     Ctrl
9 23        3D      Dia
10 32        3D     Ctrl
11 34        3D      Dia
12 43        3D     Ctrl

I want to pull the differentially expressed genes for structure (2D vs 3D) blocking by clone, and the one differentially expressed by condition (regardless of the structure variable).

I am not sure how to build the design in one call, therefore I have produced the first object to pull 2D vs 3D blocking by clone using the following.

des_structure <- DESeqDataSetFromHTSeqCount(sampleTable = samples,
                                       directory = "../data/htseq_geneCounts/",
                                       design= ~ clone+structure)

for the condition controlled by structure I have used

des_condidtion <- ddsHTSeq_diabetic <- DESeqDataSetFromHTSeqCount(sampleTable = samples,
                                                 directory = "../data/htseq_geneCounts/",
                                                 design= ~ structure+diabetic)

I am not sure whether this is the best way to build the designs.

Yes those are the appropriate designs for DESeq2. For the second design, we ignore the fact that there are correlations between pairs of samples from the same clone, because DESeq2 cannot incorporate random effects. Another approach would be limma-voom which has a duplicateCorrelation function to inform about the sample pairings that are nested within the comparison of interest (diabetic status).