### 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)

orm_dag = DagModel(**orm_dag_kwargs)
            session.add(orm_dag)
            session.flush()
            dag_run = dag.create_dagrun(
                run_type=DagRunType.SCHEDULED,
                state=DagRunState.SUCCESS,
                execution_date=dag.start_date,
                data_interval=dag.infer_automated_data_interval(dag.start_date),
                start_date=dag.start_date,
                session=session,
            )
            ti = dag_run.get_task_instance(dag_task.task_id, session)
            ti.set_state(TaskInstanceState.SUCCESS, session)
            session.flush()
            with mock.patch.object(Stats, "timing") as stats_mock:
                dag_run.update_state(session)
            metric_name = f"dagrun.{dag.dag_id}.first_task_scheduling_delay"
            if expected:
                true_delay = ti.start_date - dag_run.data_interval_end
                sched_delay_stat_call = call(metric_name, true_delay)
                assert sched_delay_stat_call in stats_mock.mock_calls
            else:
                
                sched_delay_stat_call = call(metric_name, mock.ANY)
                assert sched_delay_stat_call not in stats_mock.mock_calls
        finally:
            
            session.rollback()
            session.close()
    def test_states_sets(self, session):