How to improve machine learning model using 2+ datasets
You may wish to combine the two models using an approach called Ensemble Methods.
However, in your case I'm surprised that using the combined data in a single model is so much worse if as you say the distribution of the features is similar.
So two potential outcomes:

if the distributions of the data are the same, the it could be the individual models are overfitted and given the larger data set this is exposed.

if the distributions are different then each model is being tuned for specific features and hence a single general model is hard to achieve.


To get a better feel of what to expect, I would suggest doing some distribution plots for each feature to see if they are as you say similar.
Another simple test is to take Model A and use it to predict everything in Dataset B and vice-versa. If the actual accuracy is around 70% then everything is working as expected.