### 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)

"DagRun Finished: dag_id=%s, execution_date=%s, run_id=%s, "
                "run_start_date=%s, run_end_date=%s, run_duration=%s, "
                "state=%s, external_trigger=%s, run_type=%s, "
                "data_interval_start=%s, data_interval_end=%s, dag_hash=%s"
            )
            self.log.info(
                msg,
                self.dag_id,
                self.execution_date,
                self.run_id,
                self.start_date,
                self.end_date,
                (self.end_date - self.start_date).total_seconds()
                if self.start_date and self.end_date
                else None,
                self._state,
                self.external_trigger,
                self.run_type,
                self.data_interval_start,
                self.data_interval_end,
                self.dag_hash,
            )
            session.flush()
        self._emit_true_scheduling_delay_stats_for_finished_state(finished_tis)
        self._emit_duration_stats_for_finished_state()
        session.merge(self)
        
        return schedulable_tis, callback
    @provide_session
    def task_instance_scheduling_decisions(self, session: Session = NEW_SESSION) -> TISchedulingDecision: