I need to force my random forest model to learn one crucial relationship between features. How can I achieve that?
Machine learning algorithms are not to know, they predict. A tree-based ML algorithm will give you probabilities of being in each class, not whether it belongs to a particular class. By preparing your labels carefully; if your data has enough examples of which “feature 1 and 3 are less and equal to 100” and the result is 0, it is highly probable that your algorithm will predict its class with a strong accuracy. Obviously,  the strength of the result depends on what your ML task is, what is your data, how it is structured and what your hyperparameter settings are. I do not know if there is another way to bend the forest in such a manner. 

I know that is not the kind of an answer you expected, but I’ll be glad if I could help.