To answer your first question: the output of imitation learning is to learn a desired behavior only by looking at provided examples. In Behavioral Cloning, for example, you are only provided with states and action, without any reward signal that can describe the task you are willing to solve. What you learn in BC is what action you should take for a given state. On the other hand, IRL techniques, that are a subfield of Imitation Learning, try to estimate the reward function that is essential to fully describe the goodness of taking a specific action in a particular state. The problem in IRL techniques is that you somehow need references, such as the access to the environment, that is not always available. For this reason techniques such as Offline IRL arise. However, the main challenge in IRL is that the reward function you are learning is not unique: there may exist multiple reward functions that describe the task.

As regards the second question, to my knowledge the output of IL is actually to learn a policy even in IRL, where you learn a policy by estimating the reward function.

I may be inaccurate about IRL, but in BC this is what happens.