BUG: groupby().any() returns true for groups with timedelta all NaT
PR Summary:

This PR addresses an issue where groupby().any() returns True for groups where all Timedelta values are NaT, which is inconsistent with other dtypes (like integers and strings). For non-Timedelta dtypes, groupby().any() correctly returns False when all values in a group are null.
Reproduction:

python

import pandas as pd

# Current behavior: incorrect output for all NaT groups
df = pd.DataFrame([pd.Timedelta(1), pd.NaT]).groupby([0, 1]).any()

For other dtypes (integers and strings):

python

df = pd.DataFrame([1, None]).groupby([0, 1]).any()  # Returns False
df = pd.DataFrame(["a", None]).groupby([0, 1]).any()  # Returns False

Expected behavior for Timedelta values: groupby().any() should return False for groups where all values are NaT.
Fix Details:

    Modified the _call_cython_op function in the groupby aggregation to correctly handle Timedelta types. Now, when all values in a group are NaT, the any() method will return False.
    This ensures consistent behavior across all dtypes, including Timedelta.

Tests Added:

    A new test has been added to check that groupby().any() returns False for groups with only NaT Timedelta values and True for groups with at least one non-NaT Timedelta.

Closing:

This PR fixes the inconsistent behavior of groupby().any() for Timedelta values and improves the overall correctness of the method. Let me know if there are any additional changes needed.

Resolves: #59712

Note: I am a complete novice to open source and this is my first ever PR. I'm taking an open source systems class is college and we're suppposed to attempt to contribute to a existing project we use and I chose pandas as it is a tool that has helped me alot. So i ask for any guidance anyone can give me about any potential mistakes or improvements i can make here. Appreciate it!

Thanks for the pull request but this is already being worked on in https://github.com/pandas-dev/pandas/pull/59750. Closing to let that contributor finish the effort, but we can reopen this if it goes stale  Hi @mroeschke , could this be reopened? Hi @rhshadrach, how's this? Thanks @Petroncini! Thanks for all the help and guidance, @rhshadrach!