Consistency improvements between adk eval and AgentEvaluator

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[Contribution guide.](https://google.github.io/adk-docs/contributing-guide/)

## 🔴 Required Information
*Please ensure all items in this section are completed to allow for efficient
triaging. Requests without complete information may be rejected / deprioritized.
If an item is not applicable to you - please mark it as N/A*

### Is your feature request related to a specific problem?
Yes. In `v1.24.1`, evaluation behavior differs between `adk eval` and `AgentEvaluator.evaluate`,
which makes it harder to run and compare evals consistently across CLI and API/test workflows.

Concrete gaps I hit:
- `adk eval` has no `--num_runs`, so I need external loops to repeat runs.
- `AgentEvaluator.evaluate` does not provide built-in result persistence, so historical comparison is less convenient than CLI-based runs.
- In `adk eval`, `--config_file_path` is optional, but when omitted it falls back to in-code default criteria and does not auto-discover per-test `test_config.json` next to each eval/test file.
- `adk eval` resolves only `agent_module.agent.root_agent` and does not look for `get_agent_async`.

### Describe the Solution You'd Like
I would like feature parity/consistency between both entry points:

1. Add `--num_runs` to `adk eval` (default `1`) and aggregate results per eval case across runs.
2. Add optional result persistence support to `AgentEvaluator.evaluate` via an `EvalSetResultsManager` (local by default).
3. In `adk eval`, when `--config_file_path` is omitted, auto-discover `test_config.json` adjacent to each input eval/test file, then fall back to default criteria if no file is found.
4. Align agent resolution behavior between `adk eval` and `AgentEvaluator`: support both `root_agent` and `get_agent_async` consistently

### Impact on your work
This impacts reproducibility and operational efficiency in day-to-day evals.

- Repeated runs are needed to reduce nondeterminism, but currently require custom scripting for CLI.
- Comparing historical outcomes is easier on one path than the other.
- The same dataset/config setup behaves differently depending on whether I use CLI or programmatic evaluation.
- Agent modules that are valid in one evaluation path can fail in the other, forcing workflow-specific agent definitions.

### Willingness to contribute
Yes

---

## 🟡 Recommended Information

### Describe Alternatives You've Considered
- Keeping current differences and documenting them: this still leaves manual repetition/persistence work and split workflows.
- Wrapper scripts outside ADK: works short-term, but duplicates logic and reduces maintainability.

### Proposed API / Implementation
Pseudo-proposal:

```python
# CLI
adk eval --num_runs=3 [--config_file_path=...]

# Internals (conceptual)
for i in range(num_runs):
inference_results += run_inference(...)

aggregated = aggregate_eval_case_results(inference_results)
save_if_configured(aggregated)
```

```python
# AgentEvaluator
await AgentEvaluator.evaluate(
agent_module=...,
eval_dataset_file_path_or_dir=...,
num_runs=3,
eval_set_results_manager=LocalEvalSetResultsManager(...), # optional
)
```

```python
# Config resolution for adk eval (conceptual)
if config_file_path:
eval_config = load(config_file_path)
else:
eval_config = discover_test_config_near_input_or_default(...)
```

```python
# Conceptual shared loader used by both entry points.
agent_module = _get_agent_module(...)
if hasattr(agent_module.agent, "root_agent"):
agent = agent_module.agent.root_agent
elif hasattr(agent_module.agent, "get_agent_async"):
agent = await agent_module.agent.get_agent_async()
else:
raise ValueError("Expected `root_agent` or `get_agent_async` in agent module.")
```

### Additional Context
Goal: make eval behavior consistent regardless of entry point (`adk eval` vs `AgentEvaluator.evaluate`) so that the same eval assets produce comparable outcomes with less custom glue code.