Config-passing custom reward functions

It would be good to have a low touchpoint way to use custom reward functions without directly modifying the library sourcecode, or creating a new training recipe. Note this would apply only to the VLMEnvironment and any training recipes using it.

I think we could replace the [if/else logic](https://github.com/NVIDIA-NeMo/RL/blob/main/nemo_rl/environments/vlm_environment.py#L71) with a dict mapper (like `task_to_env` but `name_to_reward`). Then the user could pass into the config which is parsed to extract additional functions and merge them into that mapper. e.g.:
* a path to another dict mapper - either a script or module on the import path - and the name of a dict variable containing the mapper, or
* a list of path/name pairs

Note that I'm undecided whether there's any more elegance to supporting scripts or modules directly. Supporting a script should be easy because it can always `*` import any rewards from modules that the user desires. This way a user with custom reward requirements only needs one simple file colocating their reward functions.

[`verl` has a similar setup](https://github.com/volcengine/verl/blob/377bbb84f068e7ca0205ecfaf7841878c2a27b66/verl/trainer/ppo/reward.py#L42) where a python script containing functions in its scope can be imported. I believe they accept only one function. Ours can be more flexible, taking a dict or list, since the VLM environment itself handles reward function selection logic.

I am happy to contribute this. I just want to confirm whether this would be considered valuable, and whether there is any preference as to the pattern to follow.