BUG: str.cat produces NaNs when others is an Index
- [ ] I have checked that this issue has not already been reported.

- [ ] 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
print(pd.__version__)

sr = pd.Series(['a','b','c','d','e'])
others = pd.Index(['a','b','c','d','e'])

result = sr.str.cat(others=others)
print(result)

1.0.3
0    NaN
1    NaN
2    NaN
3    NaN
4    NaN
dtype: object


```

#### Problem description

The result should be the same as when `others` is a list or numpy array with the same values. The result is correct for pandas < 1.0. 

#### Expected Output

```
0    aa
1    bb
2    cc
3    dd
4    ee
dtype: object
```


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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.6.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.4.0-134-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.3
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 46.1.1.post20200322
Cython           : 0.29.15
pytest           : 5.4.1
hypothesis       : 5.7.0
sphinx           : 2.4.4
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.1
IPython          : 7.13.0
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          : 0.15.0
pytables         : None
pytest           : 5.4.1
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : 0.48.0

</details>
