BUG: nanoseconds and reso in dateutil paths
- [x] closes #55737 (Replace xxxx with the GitHub issue number)
- [x] [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.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.

Perf impact is pretty negligible compared to the cost of going through dateutil:

```
import pandas as pd
import numpy as np

dtstr = "2016/01/02 03:04:05.001000 UTC"

%timeit pd.Timestamp(dtstr)
100 µs ± 3.05 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)  # <- PR
109 µs ± 10.1 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)  # <- main

vals = np.array([dtstr] * 10**5, dtype=object)
%timeit pd.to_datetime(vals)
8.37 ms ± 134 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- PR
9.1 ms ± 597 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- main

%timeit pd.to_datetime(vals, format="mixed")
8.29 ms ± 236 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- PR
8.13 ms ± 63.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- main
```

Thanks @jbrockmendel 