you could try to apply sva to both datasets together, plot a PCA and see whether you can detect a clustering
by data set.
Usually, if there is e.g. a strong dataset specific effect, sva will capture it anyway, even though it works "unsupervised", so it might not be necessary to use Combat.
Simply apply sva and then inspect the computed surrogate variables to see whether they capture a difference bewtween the two data sets. For an example, see the capturing of the cell line effect by the surrogate variables in the RNA-Seq gene workflow:
and then include the SVs in your usual DE workflow.
As a side note, Combat has the disadvantage that it will regress the batch effect, which might lead to spurious or overoptimistic DE results, as shown by this recent paper by Nygaard et. al.:
So I personally would always prefer to include the batch effect in the model, rather than regressing it out beforehand.