Is it advisable to merge similar datasets to improve model accuracy?
Ideally if you mix data from different sources (different distributions), the mix should be uniform for each class.   Otherwise if for example class A gets more of source S and class B gets more of source T, there is a risk that the model will learn to associate class A with source S and B with source T and that it won't generalize across sources.
To validate such an approach, keep a separate test set with both sources and compute several accuracies, one for each (class, source).