BUG: OverflowError on to_json with numbers larger than sys.maxsize
- [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
import sys
from pandas.io.json import dumps

dumps(sys.maxsize)
dumps(sys.maxsize + 1)
```

#### Problem description

The Pandas JSON dumper doesn't seem to handle number values larger than `sys.maxsize` (a word). I have a dataframe that I'm trying to write to_json, but it's failing with `OverflowError: int too big to convert`. There are some numbers larger than `9223372036854775807` in it.

Passing a `default_handler` doesn't help. It doesn't get called for the error.

```python
>>> dumps(sys.maxsize)
'9223372036854775807'
>>> dumps(sys.maxsize + 1, default_handler=str)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
OverflowError: int too big to convert
```

#### Expected Output

Python's built-in json module handles large numbers without issues.

```python
>>> import json
>>> json.dumps(sys.maxsize)
'9223372036854775807'
>>> json.dumps(sys.maxsize+1)
'9223372036854775808'
```

I expect Pandas to be able to output large numbers to JSON. An option to use the built-in `json` module instead of `ujson` would be fine.

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.8.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.19.76-linuxkit
machine          : x86_64
processor        :
byteorder        : little
LC_ALL           : None
LANG             : C.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.3
numpy            : 1.18.2
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.1.1
setuptools       : 46.4.0
Cython           : None
pytest           : None
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
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.16.0
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : 0.4.2
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
