`spectral_angle_mapper` NaNs even with one zero pixel

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## 🐛 Bug

`spectral_angle_mapper` (and probably the class interface too) gives a NaN even with one zero pixel in the image.

The SAM score is the acos of the channel dot product, and requires normalising the dot by the input norms.

However in torchmetrics, the convention is to compute the metric over the whole image, and only reduce over spatial dims after. Therefore, even if there's a single pixel in the inputs that's zero, a pixel in the unreduced norm will be zero -> NaNs when one reduces.

This isn't good behaviour, because one only expects SAM to be NaN when an input is all-zeros, not just one zero pixel.

Solution could either be a) reduce ignoring NaNs; b) add an eps; c) `return reduce(unnormalised_score) / reduce(preds_norm * target_norm)`

### To Reproduce

```python
from torchmetrics.functional.image import spectral_angle_mapper
a, b = torch.ones(2, 1, 3, 8, 8)
a[:, :, 5, 3] = 0
spectral_angle_mapper(a, b)

out: tensor(nan)
```

Start from the spectral_angle_mapper functional entry point and inspect the class interface mentioned in the report. Run the provided tensor reproduction first; done means a single zero pixel no longer makes the reduced score NaN, while an all-zero input still produces NaN.

Written by the indexing model from the issue text.