Series.rmod called with scalar returns inconsistent results based on size of series
#### Code Sample, a copy-pastable example if possible

In
```
s1 = pd.Series([2] * 10001, name='long_series').rmod(-1)
s2 = pd.Series([2] * 10000, name='short_series').rmod(-1)
print(s1.loc[0:9].to_frame().join(s2.loc[0:9]))
```
Out
```
   long_series  short_series
0           -1             1
1           -1             1
2           -1             1
3           -1             1
4           -1             1
5           -1             1
6           -1             1
7           -1             1
8           -1             1
9           -1             1
```
#### Problem description

`Series.rmod` called on a scalar behaves differently depending on the size of the series.
On my machine:
* length <= 10,000 sign matches divisor (matches pythons `%` operator)
* length > 10,000 sign matches dividend

Appears to behave properly when called on other Series.
`Series.mod` does not appear to have any such issues. 

#### Expected Output

```
   long_series  short_series
0           1             1
1           1             1
2           1             1
3           1             1
4           1             1
5           1             1
6           1             1
7           1             1
8           1             1
9           1             1
```
#### Output of ``pd.show_versions()``

<details>

[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.7.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.3
numpy            : 1.17.3
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.1
setuptools       : 41.0.1
Cython           : 0.29.14
pytest           : 5.0.1
hypothesis       : None
sphinx           : 2.2.1
blosc            : None
feather          : None
xlsxwriter       : 1.2.2
lxml.etree       : 4.4.1
html5lib         : 1.0.1
pymysql          : None
psycopg2         : 2.7.3.2 (dt dec pq3 ext lo64)
jinja2           : 2.10.3
IPython          : 7.9.0
pandas_datareader: None
bs4              : 4.8.1
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.2
numexpr          : 2.7.0
odfpy            : None
openpyxl         : 3.0.0
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.10
tables           : 3.6.1
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.2.2

</details>
