MultiIndex level dropped when multiplying two series with a single entry
When multiplying two Series with overlapping MultiIndices and a _single_ entry, pandas drops levels from the MultiIndex. This doesn't happen when either Series contains multiple entries.

Using pandas 0.24.1 and Numpy 1.16.2

#### This works fine
```python
# Create Series s1, with a scope over {T, N} and set two values
index1 = pd.MultiIndex.from_product([[], []], names=['T', 'N'])
s1 = pd.Series(index=index1)
s1['T.1A', 'N.0'] = 0.5
s1['T.1B', 'N.0'] = 0.5

# Create Series s2 with a scope over {N, M} and set a single value
index2 = pd.MultiIndex.from_product([[], []], names=['N', 'M'])
s2 = pd.Series(index=index2)
s2['N.0', 'M.0'] = 0.5

# When multiplying s1 and s2 pandas will align the Series using the index.
# Note that the index has 3 levels: {N, T, M}
print(s1 * s2)

# Prints:
# N    T     M  
# N.0  T.1A  M.0    0.25
#      T.1B  M.0    0.25
# dtype: float64
```

#### This doesn't return the expected index levels
```python
# Create Series s1, with a scope over {T, N} and set a single value
index1 = pd.MultiIndex.from_product([[], []], names=['T', 'N'])
s1 = pd.Series(index=index1)
s1['T.1A', 'N.0'] = 0.5

# Create Series s2 with a scope over {N, M} and set a single value
index2 = pd.MultiIndex.from_product([[], []], names=['N', 'M'])
s2 = pd.Series(index=index2)
s2['N.0', 'M.0'] = 0.5

# Multiply s1 and s2. Correctly yields 0.25. But where did index level 'M' go!?
print(s1 * s2)

# Prints:
# T     N  
# T.1A  N.0    0.25
# dtype: float64
```
#### Problem description
Index level 'M' is dropped in the result. It appears the index from the first Series, `s1` is carried over?

#### Expected Output
I'd expect the result to have an index that is essentially the union of the indices from `s1` and `s2`.
```
# N    T     M  
# N.0  T.1A  M.0    0.25
# dtype: float64
```
#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.utf-8
LANG: None
LOCALE: en_US.UTF-8

pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.6.2
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>