### Pandas version checks - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this issue exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this issue exists on the main branch of pandas. ### Reproducible Example ``` dr = pd.Series(pd.date_range("2019-12-31", periods=1_000_000, freq="s").astype(pd.ArrowDtype(pa.timestamp(unit="ns"))), name="a") dr.to_csv("tmp.csv") pd.read_csv("tmp.csv", engine="pyarrow", dtype_backend="pyarrow", parse_dates=["a"]) ``` The read call takes 1.6 seconds, without parse dates it's down to 0.01 and pyarrow already enforces timestamp ``` int64[pyarrow] a timestamp[s][pyarrow] dtype: object ``` This was introduced by the dtype backend I guess, so would like to fix soonish ### Installed Versions <details> main </details> ### Prior Performance _No response_