BUG: pd.read_json sets wrong value for numeric column names 
- [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.

---

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

```python
df = pd.DataFrame([1.], columns=[0])
pd.read_json(df.to_json(orient="table"), orient="table")
```

#### Problem description

The returned DataFrame will be all `NaN` without exception or warning.
If the value is int as well (e.g., `df = pd.DataFrame([1], columns=[0])`, a `ValueError` is raised instead. The error message was not clear ("Cannot convert non-finite values (NA or inf) to integer"), but it's still better than returning a DataFrame with wrong values.
`NaN` is set during the call of `pd.read_json`.

#### Expected Output
Based on https://github.com/pandas-dev/pandas/issues/19129, it looks like numeric column names are not allowed in the first place. I think an exception is needed rather than setting `NaN`, which makes the debugging difficult.

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

<details>

INSTALLED VERSIONS
------------------
commit           : 9d598a5e1eee26df95b3910e3f2934890d062caa
python           : 3.7.9.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19041
machine          : AMD64
processor        : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : None.None

pandas           : 1.2.1
numpy            : 1.19.2
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 52.0.0.post20210125
Cython           : 0.29.22
pytest           : 6.2.2
hypothesis       : None
sphinx           : 3.5.2
blosc            : None
feather          : None
xlsxwriter       : 1.3.7
lxml.etree       : 4.6.2
html5lib         : 1.1
pymysql          : 1.0.2
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.20.0
pandas_datareader: None
bs4              : 4.7.1
bottleneck       : 1.3.2
fsspec           : 0.8.3
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.2
numexpr          : 2.7.3
odfpy            : None
openpyxl         : 3.0.6
pandas_gbq       : None
pyarrow          : 3.0.0
pyxlsb           : None
s3fs             : 0.4.2
scipy            : 1.6.1
sqlalchemy       : 1.3.23
tables           : 3.6.1
tabulate         : 0.8.7
xarray           : 0.16.2
xlrd             : 2.0.1
xlwt             : 1.3.0
numba            : 0.52.0

</details>
