BUG: pandas series creation fails with OverflowError when given large integers
- [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
>>> import pandas as pd
>>> pd.Series(1000000000000000000000)
0    1000000000000000000000
dtype: object
>>> pd.Series(1000000000000000000000, index = pd.date_range(pd.Timestamp.now().floor("1D"), pd.Timestamp.now(), freq='T'))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/matt/opt/anaconda3/lib/python3.7/site-packages/pandas/core/series.py", line 327, in __init__
    data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
  File "/Users/matt/opt/anaconda3/lib/python3.7/site-packages/pandas/core/construction.py", line 475, in sanitize_array
    subarr = construct_1d_arraylike_from_scalar(value, len(index), dtype)
  File "/Users/matt/opt/anaconda3/lib/python3.7/site-packages/pandas/core/dtypes/cast.py", line 1555, in construct_1d_arraylike_from_scalar
    subarr.fill(value)
OverflowError: int too big to convert
>>> pd.Series(1000000000000000000000.0, index = pd.date_range(pd.Timestamp.now().floor("1D"), pd.Timestamp.now(), freq='T'))
2020-09-11 00:00:00    1.000000e+21
2020-09-11 00:01:00    1.000000e+21
2020-09-11 00:02:00    1.000000e+21
2020-09-11 00:03:00    1.000000e+21
2020-09-11 00:04:00    1.000000e+21
                           ...
2020-09-11 11:24:00    1.000000e+21
2020-09-11 11:25:00    1.000000e+21
2020-09-11 11:26:00    1.000000e+21
2020-09-11 11:27:00    1.000000e+21
2020-09-11 11:28:00    1.000000e+21
Freq: T, Length: 689, dtype: float64

```

#### Problem description
Hi pandas, when creating a new series with very large integers, series creation fails. This is not true if you pass in a float, or if you just pass in one value of a series and no index. the traceback points to pandas code so I'm submitting a bug here.

#### Expected Output
I would expect this to fail more gracefully or either output a series of object type, or float type. when initializing an array in numpy, it transparently converts it to object type
```python
>>> np.array([1000000000000000000000]*1000).dtype
dtype('O')
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python           : 3.7.6.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.6.0
Version          : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.1.1
numpy            : 1.19.1
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.2.2
setuptools       : 49.6.0.post20200814
Cython           : 0.29.21
pytest           : 6.0.1
hypothesis       : None
sphinx           : 3.2.1
blosc            : 1.7.0
feather          : None
xlsxwriter       : 1.3.3
lxml.etree       : 4.5.2
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.18.1
pandas_datareader: None
bs4              : 4.9.1
bottleneck       : 1.3.2
fsspec           : 0.8.0
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.1
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.5
pandas_gbq       : None
pyarrow          : 1.0.1
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : 1.5.2
sqlalchemy       : 1.3.19
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
numba            : 0.50.1

</details>
