BUG: pandas.DataFrame.interpolate fails with high value of  `limit` argument
- [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
import numpy as np
df = pd.DataFrame([1]*500000)
df.iloc[1000:50000] =np.nan
df.interpolate(method='linear', limit_direction='both', limit=None)  # This runs fine eventhough the limit is effectively > 5000 datapoints
df.interpolate(method='linear', limit_direction='both', limit=5000)  # This produces an error
```

#### Problem description
An error is produced when specifying a large limit in pandas.DataFrame.Interpolate
~~The error is NOT present in pandas 1.0.1 but it is present at least in 1.0.4 and 1.0.5~~
If the limit is set to None, there is no error... even when the interpolated consecutive nans is larger than the limit that fails

Error:
```
ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.
```
The error is with Python 3.7 but not with Python 3.6

#### Expected Output
~~The expected output is what pandas v1.0.1 produces.~~

In python 3.6, specifying a large value of limit doesn't result in a ValueError

This runs in v1.0.1
```python
import pandas as pd
import numpy as np
df = pd.DataFrame([1]*500000)
df.iloc[1000:50000] =np.nan
dff = df.interpolate(method='linear', limit_direction='both', limit=5000) 
assert dff.isna().sum().values == 39000
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 32
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : None.None

pandas           : 1.0.5
numpy            : 1.18.5
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.1.1
setuptools       : 47.1.1
Cython           : 0.29.20
pytest           : 5.4.3
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.15.0
pandas_datareader: None
bs4              : 4.9.1
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.2.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.4.3
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
