[BUG] JIT GroupBy.apply aggregations with int32 dtype may return different results from pandas when large values are present

[brandon-b-miller]

Describe the bug
When executing a sum aggregation in a groupby apply UDF, the result can diverge from pandas in some cases, specifically when large values are present. This is likely due to some result not being promoted to int64 somewhere it ought to be.

Steps/Code to reproduce bug

import pandas as pd
import cudf
import numpy as np

int32_max = np.iinfo(np.int32).max

df = cudf.DataFrame(
    {
        'a':[0,0,0,0,0,0,0,0,0,0],
        'b':[int32_max,2,3,4,5,6,7,8,9,10]        
    }
)

df['b'] = df['b'].astype('int32')
pdf = df.to_pandas()

def func(group):
    return group['b'].sum()

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

print(pandas_result)
print(cudf_result)

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. The above test fails for mean, var, and std as well. Since these three reductions also involve a summation, I suspect the root cause could be the same.