[BUG] JIT GroupBy.apply corr() aggregation wrong results when one variable has zero variance

[brandon-b-miller]

Describe the bug
When executing a corr aggregation in a groupby apply UDF, the result can diverge from pandas in some cases, specifically when one of the variables to be correlated has no variance.

Steps/Code to reproduce bug

import pandas as pd
import cudf

import numpy as np

df = pd.DataFrame(
    {
        'a':[0,0,0],
        'b':[1,1,1],
        'c':[2,2,2] 
    }
)
gdf = cudf.from_pandas(df)

def func(group):

    return group['b'].corr(group['c'])

pandas_result = df.groupby('a').apply(func)
cudf_result = gdf.groupby('a').apply(func, engine='jit')

print(pandas_result)
print(cudf_result)

a
0   NaN
dtype: float64
a
0    0.0
dtype: float64

Expected behavior
Results should be the same as pandas.

Environment overview (please complete the following information)
Bare-metal, 23.10

Additional Context
came up during #13813. This must be due to mishandling the denominator in corr.