BUG: pandas to_json with orient "table" returns wrong schema & data string
- [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.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

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

```python
test = pd.DataFrame([[1,2,3],[4,5,6]], columns=[1, 2, 3])
s = test.to_json(orient="table")
print(s)
# wrong string :
# '{"schema":{"fields":[{"name":"index","type":"integer"},{"name":1,"type":"integer"},{"name":2,"type":"integer"},{"name":3,"type":"integer"}],"primaryKey":["index"],"pandas_version":"0.20.0"},"data":[{"index":0,"1":1,"2":2,"3":3},{"index":1,"1":4,"2":5,"3":6}]}'
pd.read_json(s, orient="table")
#ValueError: Cannot convert non-finite values (NA or inf) to integer
```

#### Problem description

When the initial columns are integers, the schema dict returns correct names (that unquoted integers), but the data dict identifies columns as string (quoted integers). Therefore, any loaded dataframe from this json format will return a dataframe full of empty (NaN) values or fail with an exception (I don't know which triggers which ; this minimal example here will trigger an exception ; my original dataset with multiindexes [in stackoverflow](https://stackoverflow.com/questions/65115871/pandas-read-json-with-orient-table) returned an empty dataframe...

#### Expected Output

This output for pandas.to_json(orient="table") could be read (though it is losing the "int" label key and transforming it to strings) :

'{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"1","type":"integer"},{"name":"2","type":"integer"},{"name":"3","type":"integer"}],"primaryKey":["index"],"pandas_version":"0.0.20"},"data":[{"index":0,"1":1,"2":2,"3":3},{"index":1,"1":4,"2":5,"3":6}]}'


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

<details>

INSTALLED VERSIONS
------------------
commit           : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python           : 3.6.3.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19041
machine          : AMD64
processor        : Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : fr
LOCALE           : None.None

pandas           : 1.1.4
numpy            : 1.18.4
pytz             : 2017.2
dateutil         : 2.8.1
pip              : 20.2.4
setuptools       : 36.6.0
Cython           : 0.27.2
pytest           : 3.2.3
hypothesis       : None
sphinx           : 1.6.5
blosc            : 1.5.1
feather          : 0.4.0
xlsxwriter       : 1.0.2
lxml.etree       : 4.1.0
html5lib         : 0.9999999
pymysql          : None
psycopg2         : None
jinja2           : 2.9.6
IPython          : 6.2.1
pandas_datareader: None
bs4              : 4.6.0
bottleneck       : 1.2.1
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 2.2.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.7.1
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.1.14
tables           : None
tabulate         : 0.8.5
xarray           : 0.9.6
xlrd             : 1.1.0
xlwt             : None
numba            : 0.35.0

</details>
