How to find feature importance with multiple XGBoost models
I suspect you may be confusing the terms "test set" and "training set". Normally, the model is trained on the training set, and evaluated on the test set. The feature importance is independent of the test set, it's a property of the model that you trained.

The obvious answer is to combine all months into a single training dataset, and train a model on that, but you say it’s not an option.

Then I think your only option is to combine the results from individual months, for example by summing or averaging them and presenting the final ranking. Whether this is valid depends on the criteria used to train the trees (how the splits are determined). If there is a valid way to combine them, I still recommend against it, because I suspect it will obscure reality and discussion over the methods may distract from the result.

So I would sidestep the problem by presenting a graph like is sometimes shown for sports leagues or polls before elections. On the horizontal axis are the months, on the vertical axis the rankings. This way you visualize all info easy to grasp and you also show any trends and seasonal effects.