May the training sets of two predictive modeling cases (with an overlap in features) be combined?
Well, there is a  obvious trade-off of columns vs rows. Why don't you first check the importance of columns C and D in the individual models? The less relevant they are, the better idea it is to throw them away and comine the tables.
For example, you can try a random forest with sklearn and then check feature_importances_.