DESeq2 complicated design - effect of replicated samples
I agree with @devon-ryan that the design matrix looks reasonable, i.e. that it's okay to treat the new samples as biological replicates of the original batch, and that the batch number should be included in that matrix (assuming DESeq2 doesn't complain about it) to filter out most of the batch effects.

DESeq2 tends to be conservative in its differential expression calculations. While it's true that more replicates in one sample will increase the number of differentially-expressed genes that are statistically significant (because the biological variation can be better modelled), I wouldn't expect this to change the overall picture.

However, whole-genome differential expression testing should not be used on its own to demonstrate biological relevance. I recommend as a first-pass sanity check that people compare any differential expression results to what is seen on a genome browser to identify any quirky things going on (e.g. expression across only a part of the gene). Ideally, results should be confirmed with targeted analyses on the same (or similar) biological samples, either at the transcript level (e.g. qPCR) or at the protein level (e.g. Western Blot). When aggregating evidence from multiple analyses, the specific way in which one of those analyses was done is less important.

Aside: It'd obviously be better [in the future] to make sure that your sample replicates are both robust to drop-out, and allow for outliers to be noticed, reducing the batch-effect problem. In practise this means a minimum of four replicates per informative condition, but because funders / researchers are well known for only choosing the minimum, I'd just state that six replicates per sample as what is needed to produce good results.