Can we use two independently measured features in a same ML model?
From the example I assume that an instance corresponds to a user, and you have both full sequences of the mouse and keyboard as features for predicting the user. I can think of two options for using these features in the same model:

With feature engineering, find a way to represent both sequences as a fixed array of features. For example you might have features such as average typing speed average mouse speed, number of mouse movements, number of times each key is pressed, etc.
Similar idea but in a more DL approach: find a way to represent both sequences as embeddings (there are methods for word embeddings, sentence embeddings, graph embeddings...)

In my opinion the main issue is the variable length of the sequences, not the fact that they are not aligned (the alignment would matter if the target variable was for one element in the sequence).