Feature to mark invalid outputs from the reward system

**Is your feature request related to a problem? Please describe.**

In `math_environment.py` and it will assign `0` reward if exceptions are raised. It may be true for this math case, but in general, should we skip such examples and use `None` or `np.nan` instead (assuming `len(results) == len(pred_responses)` has to be true?)

https://github.com/NVIDIA/NeMo-RL/blob/cec9a60ff798554279ca494a0ef40ce5f283e0d8/nemo_rl/environments/math_environment.py#L87-L92

```
def verify(
self, pred_responses: List[str], ground_truths: List[str]
) -> List[float]:
"""Verify the correctness of the predicted responses against the ground truth.

Args:
pred_responses: List[str]. The predicted responses from the LLM.
ground_truths: List[str]. The ground truth responses.

Returns:
List[float]. The rewards for each predicted response.
"""
results = []
for response, ground_truth in zip(pred_responses, ground_truths):
try:
ground_truth_parsable = "\\boxed{" + ground_truth + "}"
with _mute_output():
try:
ret_score, _ = self.verify_func(
[ground_truth_parsable], [response]
)
except Exception:
ret_score = 0.0 <= Should we skip if detection is failed?

results.append(float(ret_score))
except Exception:
results.append(0.0) <= Same here
return results
```

**Describe the solution you'd like**

Per our internal discussion, adding a feature to mark `invalid` outputs to mask out samples in training might be a good solution.

**Describe alternatives you've considered**

N/A

**Additional context**

N/A