it sounds like your design matrix for this measurement looks like this:

Count ~ Condition + Run

Do you have any other factors that are worth including that may contribute to any batch effects? For example, do you have kidney cells in the first batch and lung cells in the second batch?

Splitting samples across multiple runs is common for larger datasets, so this is okay. You don't need to combine the counts together to smooth out batch effects. In my opinion, it is better to explicitly include the batch label, even if your individual measurements are well-designed (e.g. 3 control + 3 tumour in the first run and 4 control + 4 tumour in the second run).

You can combine all the counts together into a single DESeqDataSet object using the DESeqDataSetFromMatrix() function if you want to run DESeq and get results from the differential analysis. I would recommend doing that and specifying the design parameter, using the design formula above.

From there, you could test out batch effect adjustment/removal algorithms and use something like a principal component analysis (PCA) plot or relative log expression (RLE) plot to visually diagnose if you have any weird data points that can't be explained by the disease condition or sequencing run.