[FEA] Don't fallback to pandas after simpe `hasattr` check

Discovered in [Narwhals](https://github.com/rapidsai/cudf/issues/17666#issuecomment-2568524622)

If I have a function like:
```python
def process_df(df):
if hasattr(df, 'with_columns'):
# Do something. cudf/pandas wouldn't go here
return df.with_columns(...)
return df.groupby('a')['b'].sum()
```
and run it with `cudf.pandas`, then this is enough to cause a fallback to pandas - even though cudf would have been perfectly capable of executing it!

This isn't farfetched - you can find examples of libraries doing hasattr checks all over the place, e.g. this one in pymc

https://github.com/pymc-devs/pymc/blob/ae4a6292aa48d5722dba6ab422e1a3d895ca7bf7/pymc/data.py#L232-L248

Scikit-learn:

https://github.com/scikit-learn/scikit-learn/blob/c9aeb15f8f1c7c54ed4ef27c871f7167e2ce3077/sklearn/feature_selection/_base.py#L129-L131

Plotly:

https://github.com/plotly/plotly.py/blob/231edaa61f8590f715134d42ea0bc2f858dd713e/packages/python/plotly/plotly/express/_imshow.py#L303-L312

---

I asked about this on Slack, and got the response

> I think we still need to fall back because we need to raise the same AttributeError that pandas would raise if it didn’t exist.

As a user, I consider falling back to pandas unnecessarily to be significantly worse than raising a slightly inconsistent AttributeError message - especially when using `hasattr` which is when the `AttributeError` would be caught anyway.

If exception raising needs to be perfectly consistent, perhaps there's a way to still raise it without needing to fallback to pandas? e.g.
```python
def __getattr(self, attr):
if hasattr(pandas.DataFrame, attr):
# do the fallback, raise if necessary
else:
msg = f"{attr} is present in neither cuDF nor pandas"
raise AttributeError(msg)
```