ValueError from operations between Series with specific names 
The following raises a ValueError due to comparison of the two names in the function _maybe_match_name (core/ops/common.py).

```python
>>> from pandas import Series
>>> import numpy as np
>>> series_a = Series([1,2,3], name=np.float64(1.0))
>>> series_b = Series([1,2,3], name=(np.float64(1.0), np.float64(2.0)))
>>> series_a + series_b
```

It seems to only occur when comparison a tuple of numpy dtypes to a single numpy dtype, but I didn't test every possible combination. It does not occur when using built-in floats, I imagine this is due to overloading of the == operator by the numpy dtypes when the other operand is a tuple.

There is a nearby TODO "what if they both have np.nan for their names?" I don't know how feasible it is to consider every case where equality fails, but it seems a try/except returning None would seem to resolve this issue, and be in line with the default return value if the names do not compare equal. I would be keen to implement this simple fix if it seems sensible.

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.8.0.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : English_United Kingdom.1252
pandas           : 1.0.3
numpy            : 1.19.0
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.2.4
setuptools       : 47.3.1
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.5.0
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.19.0
pandas_datareader: 0.8.1
bs4              : 4.9.1
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.5.0
matplotlib       : 3.2.2
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : 1.5.0
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : 0.51.2