parallel cumulative_eval returns incorrect results

[jtanx]

Checks

• I have checked that this issue has not already been reported.
• I have confirmed this bug exists on the latest version of Polars.

Reproducible example

import polars as pl

print(pl.__version__)

df = pl.LazyFrame({"A":range(1, 10)}, strict=False)
df.select(pl.col("A").cumulative_eval(pl.element().last(), parallel=True)).collect()

Log output

1.8.2
shape: (9, 1)
A
i64
9
9
9
9
9
9
9
9
9

Issue description

This looks like a regression from polars 1.7.1

Expected behavior

Output should match result when parallel=False i.e.

1.7.1
shape: (9, 1)
A
i64
1
2
3
4
5
6
7
8
9

Installed versions

--------Version info---------
Polars:              1.8.2
Index type:          UInt32
Platform:            Linux-6.8.0-45-generic-x86_64-with-glibc2.39
Python:              3.12.3 (main, Sep 11 2024, 14:17:37) [GCC 13.2.0]

----Optional dependencies----
adbc_driver_manager  <not installed>
altair               <not installed>
cloudpickle          <not installed>
connectorx           <not installed>
deltalake            <not installed>
fastexcel            <not installed>
fsspec               <not installed>
gevent               <not installed>
great_tables         <not installed>
matplotlib           3.9.2
nest_asyncio         1.6.0
numpy                2.1.1
openpyxl             <not installed>
pandas               <not installed>
pyarrow              <not installed>
pydantic             <not installed>
pyiceberg            <not installed>
sqlalchemy           <not installed>
torch                <not installed>
xlsx2csv             <not installed>
xlsxwriter           <not installed>

[coastalwhite]

This is a regression caused by #18664.