Why the max_depth of every decision tree in my random forest classifier model are the same?
If i am not mistaken, a decision tree is likely to reach its max depth. There is nothing wrong with it. I would even say that he surely will. The space you allow your tree to grow in, the space your tree will occupy. 
Scaled to a random forest, again there is nothing wrong with it. You should focus on choosing the right max_depth, because with a grater max_depth comes a greater risk of over fitting.
Try different values and compare how you are doing with your test data.
Why the max_depth of every decision tree in my random forest classifier model are the same?
I set the max_depth=30 of my RandomForestClassifier, and when I print each trees(trees = RandomForestClassifier.estimators_), I find every tree's max_depth are the same.
I really don't know where is the problem and how it happnend.