Handling a feature containing multiple values
You need to rethink your approach. Rather than "what can I code to make things work", you need to ask "what is the right thing to do, and how can I implement this".

Clustering is hard (easy to run, hard to get good results).

Clustering non-continuous data is even harder.

The reason why people create a lot of false results here is because you have infinitely many way to weight different values and attributes, and essentially you can get almost any result you want just by playing around with parameters. Don't let yourself be lured by the common hack of "one hot encoding" everything; it just shows that people don't (want to) know what they are doing.

To get a reliable result, you need to be very clear on your assumptions, such as "I assume all actors are equally representative, and the overlap as measured by Jaccard is a good indicator of similarity" (I would disagree here, actors should be weighted).
Then you need to do the same thing with genres. Here it is even more questionable hos to compute similarity of genres.
And after that, you need to combine all these different similarities into one. That is probably the hardest step, and will involve even more weighting parameters.

All in all, I'd say: whatever you do, the clustering is not statistically sound. It's too many parameters chosen without even a good reason (yet a proof) that this way is better than another way of choosing them.
In particular, you will never know if there is an even better clustering with different parameters.