ENH: Add fast array equal function for indexers
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.

The all equal case is as fast as numpy on 2 million entries, the early exist is significantly faster obviously

Scrolling through issues, it looks like this is basically #32339.
(Takeaway from there was that perf for non short-circuit case needed to be checked. It's pretty hard to beat numpy's SIMD extensions.) Commented on this in the initial post. Both are running around 700micro seconds if the whole array is checked (2 million entries). Ours is actually a bit faster, but not by much. 

Numpy is doing some things that are more expensive, e.g. allocating a Boolean array and then calling all on it. +

Edit: Looks like NumPy is faster with less elements, but not by that much:

```
10**6
%timeit np.array_equal(left, right)
269 µs ± 5.64 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
%timeit array_equal_fast(left, right)
303 µs ± 2.44 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
```

and with 2 Million:
```
%timeit array_equal_fast(left, right)
701 µs ± 79.4 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
%timeit np.array_equal(left, right)
729 µs ± 7.5 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
```

Edit2: I'd try using this only for indexers in CoW in the beginning. The advantage is, that if an indexer is equal, we avoid a copy which brings a big speedup. And if it's not equal, we should find different values early on.
