### 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)

return (
                    bool(self.tis)
                    and all(not t.task.depends_on_past for t in self.tis)
                    and all(t.task.max_active_tis_per_dag is None for t in self.tis)
                    and all(t.state != TaskInstanceState.DEFERRED for t in self.tis)
                )
            def recalculate(self) -> _UnfinishedStates:
                return self._replace(tis=[t for t in self.tis if t.state in State.unfinished])
        start_dttm = timezone.utcnow()
        self.last_scheduling_decision = start_dttm
        with Stats.timer(f"dagrun.dependency-check.{self.dag_id}"):
            dag = self.get_dag()
            info = self.task_instance_scheduling_decisions(session)
            tis = info.tis
            schedulable_tis = info.schedulable_tis
            changed_tis = info.changed_tis
            finished_tis = info.finished_tis
            unfinished = _UnfinishedStates.calculate(info.unfinished_tis)
            if unfinished.should_schedule:
                are_runnable_tasks = schedulable_tis or changed_tis
                
                if not are_runnable_tasks:
                    are_runnable_tasks, changed_by_upstream = self._are_premature_tis(
                        unfinished.tis, finished_tis, session
                    )
                    if changed_by_upstream:  
                        unfinished = unfinished.recalculate()
        leaf_task_ids = {t.task_id for t in dag.leaves}