### 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)

for cur_dag_id in dag_ids:
            for cur_run_id in run_ids:
                
                dag_task_id_groups = task_id_groups[(cur_dag_id, cur_run_id)]
                dag_map_index_groups = map_index_groups[(cur_dag_id, cur_run_id)]
                if len(dag_task_id_groups) <= len(dag_map_index_groups):
                    for cur_task_id, cur_map_indices in dag_task_id_groups.items():
                        filter_condition.append(
                            and_(
                                TaskInstance.dag_id == cur_dag_id,
                                TaskInstance.run_id == cur_run_id,
                                TaskInstance.task_id == cur_task_id,
                                TaskInstance.map_index.in_(cur_map_indices),
                            )
                        )
                else:
                    for cur_map_index, cur_task_ids in dag_map_index_groups.items():
                        filter_condition.append(
                            and_(
                                TaskInstance.dag_id == cur_dag_id,
                                TaskInstance.run_id == cur_run_id,
                                TaskInstance.task_id.in_(cur_task_ids),
                                TaskInstance.map_index == cur_map_index,
                            )
                        )
        return or_(*filter_condition)
    @classmethod
    def ti_selector_condition(cls, vals: Collection[str | tuple[str, int]]) -> ColumnOperators: