BUG: to_timedelta() won't accept all freq units returned by infer_freq()
- [ x ] I have checked that this issue has not already been reported.

- [ x ] I have confirmed this bug exists on the latest version of pandas.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

#### Code Sample, a copy-pastable example

```python
datetime_idx = pd.date_range('07-10-16 00:00', periods = 4, freq = '2H')
td_infer = pd.to_timedelta(pd.infer_freq(datetime_idx) ) # Fine

datetime_idx = pd.date_range('07-10-16 00:00', periods = 4, freq = 'H')
td_infer = pd.to_timedelta(pd.infer_freq(datetime_idx) ) # Fails.... : ValueError: unit abbreviation w/o a number

datetime_idx = pd.date_range('07-10-16 00:00', periods = 4, freq = '1H') # datetime drops to 'H'
td_infer = pd.to_timedelta(pd.infer_freq(datetime_idx) ) # Fails.... : ValueError: unit abbreviation w/o a number

td_infer = pd.to_timedelta('1' + pd.infer_freq(datetime_idx) ) # Works...
```

#### Problem description

to_timedelta() rejects unitary time units like 'H', 'D' etc which infer_freq() returns. Strings like '2H' etc are accepted. Even if you create the date range with '1H' freq it is dropped back to 'H' by date_range(). So you are forced to handle this special case and prepend a '1' which feels very clumsy.

#### Expected Output

to_timedelta() should treat strings like 'H' as for '1H' - so output of infer_freq() can be safely passed through.

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python           : 3.6.4.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.6.0
Version          : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None
pandas           : 1.1.2
numpy            : 1.19.2
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.2.3
setuptools       : 28.8.0
Cython           : None
pytest           : 6.0.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : 1.5.2
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
