Multiplying nd.array by series returns incorrect result in pandas 0.23.0-0.23.4 (but is fixed in 0.24)
#### Code Sample, a copy-pastable example if possible

```python
In [1]: import numpy as np
   ...: import pandas as pd
   ...:
   ...: mat = np.random.random(size = (100,100))
   ...: arr = np.random.random(size=100)
   ...:
   ...: df = pd.DataFrame(mat)
   ...: ser = pd.Series(arr)
   ...:
   ...: print((mat @ arr)[:5])
   ...: print((mat @ ser)[:5])
   ...: print((df @ ser).values[:5])
   ...:
   ...: print(np.__version__)
   ...: print(pd.__version__)
[25.58954419 27.14049632 29.35058172 25.60100699 28.81915819]
[25.23415259 27.21156925 25.54180341 24.80747474 27.19878459] # !!!!!!!
[25.58954419 27.14049632 29.35058172 25.60100699 28.81915819]
1.16.4
0.23.4
```

#### Problem description

I'm not sure why this happens, but if you multiply an nd.array by a pd.Series, you get the wrong result returned in pandas `0.23.0 - 0.23.4`. It fails silently, which is *bad*. Even throwing a warning when this happens for `0.23` would be useful, because it takes some packages a while to get up to date with the major API changes in 0.24.

The problem no longer occurs with an upgrade to 0.24

I have tried this with numpy versions as far back at `1.15.0` and all fail in this manner. I have reproduced on Linux and MacOS machines.

Although this is fixed in an update, the silent return of a numerically incorrect result feels like it should be addressed.

#### Expected Output

The correct result.

#### Output of ``pd.show_versions()``
This is for the Macbook Pro, although error still occurs on Linux.
<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.4
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.2
numpy: 1.15.0
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 6.1.0
sphinx: 1.8.5
patsy: None
dateutil: 2.6.0
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.4
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
