BUG: read_csv raising for arrow engine and parse_dates
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
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Not sure if this is an actual regression, but ties a bit into the dtype backend and would be nice if this works, since this raises for every parse_date case with numpy dtype backend (and I want to advertise the engine in my pyarrow blog...)
Can you share a MRE of the issue you're having?

I'm wondering if #50056 fixes/causes the issue. ```
data = """a,b
2000-01-01 00:00:00,1
2000-01-01 00:00:01,1"""

result = pd.read_csv(StringIO(data), parse_dates=["a"], engine="pyarrow")
```

Nope, still raises after your PR is in

Edit: The main issue is that arrow infers as datetime and we try to infer again, which raises. Thanks @phofl  Owee, I'm MrMeeseeks, Look at me.

There seem to be a conflict, please backport manually. Here are approximate instructions:

1. Checkout backport branch and update it.

```
git checkout 2.0.x
git pull
```

2. Cherry pick the first parent branch of the this PR on top of the older branch:
```
git cherry-pick -x -m1 aaf503784a7cbf6425d681551d1e1e686ce14815
```

3. You will likely have some merge/cherry-pick conflict here, fix them and commit:

```
git commit -am 'Backport PR #53295: BUG: read_csv raising for arrow engine and parse_dates'
```

4. Push to a named branch:

```
git push YOURFORK 2.0.x:auto-backport-of-pr-53295-on-2.0.x
```

5. Create a PR against branch 2.0.x, I would have named this PR:

> "Backport PR #53295 on branch 2.0.x (BUG: read_csv raising for arrow engine and parse_dates)"

And apply the correct labels and milestones.

Congratulations — you did some good work! Hopefully your backport PR will be tested by the continuous integration and merged soon!

Remember to remove the `Still Needs Manual Backport` label once the PR gets merged.

If these instructions are inaccurate, feel free to [suggest an improvement](https://github.com/MeeseeksBox/MeeseeksDev).
                 Will backport later today or tomorrow  Might want to consider backporting #50056 and #52087 too. The first one is a bit less common, can think about the second one if you like That'd be great, I think there were a couple issues opened about nans in string columns not being read properly related to the second.