More features hurts when underfitting?
Theoretically speaking, more data leads to better model. However, in practice, more features often leads to the difficulty of model training.

Assuming there are 30 "main" features of your dataset. Feature set A contains 20 "main" features, so it might be easy (20 out of 26) for one "main" feature to be "chosen" and "trained", under certain hyperparameters (in your case, 100 trees). When it comes to feature set B, which contains all "main" features, it's hard (30 out of 96) for one "main" feature to be chosen, and it's harder when there's only 100 trees (cause there are 66 "minor" features which should not be trained, relatively). That's what we called "under-fitting".

Back to your question, when the model is under-fitting, if we're lucky that model trained on feature set B (namely model B) contains all these 30 "main" features, the model will be good, and we might not able to find out it's under-fitting. But in most cases, we're not that lucky, model can be ruined with all 66 "minor" features, relatively.

In my ML practice, I'll try more training iterations when more samples come, and try a more complex model when more features come, with the control of over-fitting/under-fitting.