### Apache Airflow version

2.4.3

### What happened

A significant fraction of the DAG Runs of a DAG that has 2+ consecutive mapped tasks which are are being passed an empty list are marked as failed when all tasks are either succeeding or being skipped. This was supposedly fixed with issue #25200 but the problem still persists.
![image](https://user-images.githubusercontent.com/46539900/203193331-db94c793-36e8-4fbd-bc45-29865c44fbfc.png)

### What you think should happen instead

The DAG Run should be marked success.

### How to reproduce

The real world version of this DAG has several mapped tasks that all point to the same list, and that list is frequently empty. I have made a minimal reproducible example.

```
from datetime import datetime

from airflow import DAG
from airflow.decorators import task


with DAG(dag_id="break_mapping", start_date=datetime(2022, 3, 4)) as dag:

    @task
    def add_one(x: int):
        return x + 1

    @task
    def say_hi():
        print("Hi")

    @task
    def say_bye():
        print("Bye")

    added_values = add_one.expand(x=[])
    added_more_values = add_one.expand(x=[])
    added_more_more_values = add_one.expand(x=[])
    say_hi() >> say_bye() >> added_values
    added_values >> added_more_values >> added_more_more_values
```

### Operating System

Debian Bullseye

### Versions of Apache Airflow Providers

_No response_

### Deployment

Astronomer

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

def test_ti_scheduling_mapped_zero_length(dag_maker, session):
    with dag_maker(session=session):
        task = BaseOperator(task_id="task_1")
        mapped = MockOperator.partial(task_id="task_2").expand(arg2=task.output)
    dr: DagRun = dag_maker.create_dagrun()
    ti1, ti2 = sorted(dr.task_instances, key=lambda ti: ti.task_id)
    ti1.state = TaskInstanceState.SUCCESS
    session.add(
        TaskMap(dag_id=dr.dag_id, task_id=ti1.task_id, run_id=dr.run_id, map_index=-1, length=0, keys=None)
    )
    session.flush()
    decision = dr.task_instance_scheduling_decisions(session=session)
    
    
    assert decision.finished_tis == [ti1, ti2]
    indices = (
        session.query(TI.map_index, TI.state)
        .filter_by(task_id=mapped.task_id, dag_id=mapped.dag_id, run_id=dr.run_id)
        .order_by(TI.map_index)
        .all()
    )
    assert indices == [(-1, TaskInstanceState.SKIPPED)]
@pytest.mark.parametrize("trigger_rule", [TriggerRule.ALL_DONE, TriggerRule.ALL_SUCCESS])
def test_mapped_task_upstream_failed(dag_maker, session, trigger_rule):
    from airflow.operators.python import PythonOperator
    with dag_maker(session=session) as dag:
        @dag.task
        def make_list():
            return list(map(lambda a: f'echo "{a!r}"', [1, 2, {"a": "b"}]))
        def consumer(*args):