BUG: Can't do merge_asof with tolerance and pyarrow dtypes

[0x26res]

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Reproducible Example

import datetime

import pandas as pd

df = pd.DataFrame(
    {
        "timestamp": pd.Series(
            [datetime.datetime(2023, 1, 1)], dtype="timestamp[us, UTC][pyarrow]"
        ),
        "value": ["1"],
    }
)

pd.merge_asof(df, df, on="timestamp") # OK
pd.merge_asof(df, df, on="timestamp", tolerance=pd.to_timedelta("1s")) # fails
# error is: pandas.errors.MergeError: key must be integer, timestamp or float

Issue Description

support for pyarrow dtypes was recently added to pd.merge_asof (#52904). It works fine, excepted when I specify a tolerance argument. Then it throws pandas.errors.MergeError: key must be integer, timestamp or float

Expected Behavior

It should work as expected, just like it does with datetime64[ns], but there's probably some conversion work involved.

Installed Versions

INSTALLED VERSIONS

commit : a671b5a
python : 3.10.13.final.0
python-bits : 64
OS : Darwin
OS-release : 23.1.0
Version : Darwin Kernel Version 23.1.0: Mon Oct 9 21:27:24 PDT 2023; root:xnu-10002.41.9~6/RELEASE_ARM64_T6000
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8

pandas : 2.1.4
numpy : 1.26.2
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 69.0.2
pip : 23.3.1
Cython : None
pytest : 7.4.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.3
html5lib : None
pymysql : None
psycopg2 : 2.9.5
jinja2 : 3.1.2
IPython : 8.16.1
pandas_datareader : 0.10.0
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat: None
fastparquet : None
fsspec : 2023.12.1
gcsfs : None
matplotlib : 3.8.2
numba : None
numexpr : 2.8.7
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 14.0.1
pyreadstat : None
pyxlsb : None
s3fs : 2023.12.1
scipy : 1.11.4
sqlalchemy : 2.0.9
tables : None
tabulate : 0.9.0
xarray : 2023.7.0
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None