Feature importance difference in two similar machine learning models
In 2nd case, you are not comparing apple to apple.
Let's say we have 4 Features and all are equally good [Also, No interaction].
Case I -
We created two Models using 2 Features each
Model I - F1/F2
Model II- F3/F4
Comparing these two Models as Features will give you an Idea about F1/F2-combined compared to F3/F4-combined. This is your Case - I.
Case II -
If you will compare F1/F2-combined, F3(alone), and F4(alone), definitely F1/F2 combined will have high importance.
 The output probability of a Model using two Features is basically holding the information of both the Features together.