Incorrect result in computing `MulticlassRecall` macro average when `ignore_index` is specified

@rittik9 is already working on this.

Since Aug 29, 2024.

## 🐛 Bug

Specifying the `ignore_index` argument in `MulticlassRecall` leads to incorrect results when computing the macro average.

### To Reproduce

```python
import torch
from torchmetrics.classification.precision_recall import MulticlassRecall

metric = MulticlassRecall(num_classes=2, ignore_index=0, average="macro")
y_true = torch.tensor([0, 0, 1, 1])
y_pred = torch.tensor([
[0.9, 0.1], # Correct
[0.9, 0.1], # Correct
[0.9, 0.1], # Incorrect
[0.1, 0.9], # Correct
])
metric.update(y_pred, y_true)
print(metric.compute()) # Prints 0.25, but I would expect the result to be 0.5
```

### Expected behavior

In the toy example above there are two classes (0 and 1) and two instances of each class. I want to ignore class 0 altogether (hence `ignore_index=0`), so I would expect the first two predictions to be ignored entirely (since they relate to class 0 instances). Of the remaining two predictions only one is correct, so I would expect the recall score to be `0.5`.

However, the actual result is `0.25`. It looks as if the recall score for the `ignore_index` class is simply set to 0 before computing the average, causing the average to be computed as `(0 + 0.5) / 2` instead of `0.5 / 1`.

### Environment

- TorchMetrics version: 1.3.1 (installed via pip)
- Python version: 3.10
- PyTorch version: 2.2.1
- OS: Ubuntu 22.04