### Apache Airflow version

main (development)

### What happened

Task failing with error "missing upstream values" when running a dag with dynamic task group mapping
error -
```
[2022-11-23, 15:50:07 UTC] {abstractoperator.py:456} ERROR - Cannot expand <Task(_PythonDecoratedOperator): increment_and_verify.hello_there> for run manual__2022-11-23T15:49:59.771915+00:00; missing upstream values: ['x']
```

### What you think should happen instead

It should succeed 

### How to reproduce

Run the below dag code 
```
from airflow import DAG
from airflow.models.taskinstance import TaskInstance
from airflow.operators.dummy import DummyOperator
from datetime import datetime, timedelta
from airflow.decorators import task, task_group
with DAG(
    dag_id="taskmap_taskgroup",
    tags=["AIP_42"],
    start_date=datetime(1970, 1, 1),
    schedule_interval=None,
) as dag:

    @task
    def onetwothree():
        return [1,2,3]

    @task
    def hello_there(arg):
        print(arg)

    @task_group
    def increment_and_verify(x):
        hello_there(x)

    increment_and_verify.expand(x=onetwothree())>>DummyOperator(task_id="done")
```

### Operating System

mac os

### Versions of Apache Airflow Providers

_No response_

### Deployment

Virtualenv installation

### 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)

if key.dag_id != node.dag_id or key.task_id != node.task_id or key.run_id != run_id:
                        
                        return True
                    return 0 <= key.map_index <= max_map_index
                
                ti_status.to_run = {key: ti for (key, ti) in ti_status.to_run.items() if to_keep(key)}
                ti_status.to_run.update({ti.key: ti for ti in new_mapped_tis})
                for new_ti in new_mapped_tis:
                    new_ti.set_state(TaskInstanceState.SCHEDULED, session=session)
            
            for ti in ti_status.running.values():
                if self.disable_retry and ti.state == TaskInstanceState.UP_FOR_RETRY:
                    ti.set_state(TaskInstanceState.FAILED, session=session)
            
            self._update_counters(ti_status=ti_status, session=session)
            session.commit()
            
            _dag_runs = ti_status.active_runs[:]
            for run in _dag_runs:
                run.update_state(session=session)
                if run.state in State.finished:
                    ti_status.finished_runs += 1
                    ti_status.active_runs.remove(run)
                    executed_run_dates.append(run.execution_date)
            self._log_progress(ti_status)
            session.commit()
        
        return executed_run_dates