`ModelBuilder` missing `resource_requirements` and `inference_component_name` fields for `CustomOrchestrator` IC deployment

**PySDK Version**
- [ ] PySDK V2 (2.x)
- [x] PySDK V3 (3.x)

**Describe the bug**

When deploying a `CustomOrchestrator` as an Inference Component — as documented in the [Build and deploy AI inference workflows with new enhancements to the Amazon SageMaker Python SDK](https://aws.amazon.com/blogs/machine-learning/build-and-deploy-ai-inference-workflows-with-new-enhancements-to-the-amazon-sagemaker-python-sdk/) blog post — the `ModelBuilder` class internally reads `self.resource_requirements` and `self.inference_component_name` during `build()` to determine:

1. Whether to deploy the orchestrator as an IC or a standalone Endpoint ([model_builder.py L4507](https://github.com/aws/sagemaker-python-sdk/blob/44410e2c55202f80d980e1dfd4533e66b9d44495/sagemaker-serve/src/sagemaker/serve/model_builder.py#L4507))
2. The IC name and resource allocation to include in the deployable spec ([model_builder.py L4534-4536](https://github.com/aws/sagemaker-python-sdk/blob/44410e2c55202f80d980e1dfd4533e66b9d44495/sagemaker-serve/src/sagemaker/serve/model_builder.py#L4534))

However, **neither `resource_requirements` nor `inference_component_name` are defined as dataclass fields** on `ModelBuilder`. They cannot be passed through the constructor, despite being required for the documented `CustomOrchestrator` workflow.

The `ModelBuilder` dataclass fields are:

```
model, model_path, inference_spec, schema_builder, modelbuilder_list, role_arn,
sagemaker_session, image_uri, s3_model_data_url, source_code, env_vars, model_server,
model_metadata, log_level, content_type, accept_type, compute, network, instance_type,
mode, shared_libs, dependencies, image_config
```

The existing `compute` field is a `Compute(ResourceConfig)` class designed for training jobs (volume sizes, spot training, instance groups) — not inference component resource allocation.

**To reproduce**

Try the following script:

```python
from sagemaker.serve.model_builder import ModelBuilder, SchemaBuilder
from sagemaker.serve.spec.inference_base import CustomOrchestrator
from sagemaker.core.inference_config import ResourceRequirements
from sagemaker.core.helper.session_helper import Session, get_execution_role

class MyOrchestrator(CustomOrchestrator):
def __init__(self, endpoint_name, component_names):
super().__init__()
self.endpoint_name = endpoint_name
self.component_names = component_names

def handle(self, data, context=None):
import json
response = self.client.invoke_endpoint(
EndpointName=self.endpoint_name,
InferenceComponentName=self.component_names[0],
Body=data if isinstance(data, (str, bytes)) else json.dumps(data),
ContentType="application/json"
)
return json.loads(response["Body"].read())

role = get_execution_role()
sess = Session()

# This is the expected usage pattern per the reference notebook:
orchestrator = ModelBuilder(
inference_spec=MyOrchestrator(
endpoint_name="my-endpoint",
component_names=["base-ic", "adapter-ic"],
),
dependencies={"auto": False, "custom": ["cloudpickle"]},
sagemaker_session=sess,
role_arn=role,
schema_builder=SchemaBuilder(sample_input="Test", sample_output={"generated_text": "test"}),
resource_requirements=ResourceRequirements(
requests={"memory": 4096, "num_accelerators": 1, "copies": 1, "num_cpus": 2}
),
inference_component_name="my-orchestrator-ic",
)
```

**Expected behavior**

`ModelBuilder` accepts `resource_requirements` and `inference_component_name` as constructor parameters, consistent with the documented usage in the [Llama3.1-Mistral inference workflow reference notebook](https://github.com/aws-samples/sagemaker-genai-hosting-examples/blob/main/Llama3.1-Mistral-workflow/Llama3.1-8B-Mistral-7B-inference-orchestrator.ipynb) (Cell 15):

```python
orchestrator = ModelBuilder(
inference_spec=PythonCustomInferenceEntryPoint(...),
dependencies={"auto": False, "custom": ["cloudpickle", "graphene"]},
sagemaker_session=Session(),
role_arn=role,
resource_requirements=ResourceRequirements(
requests={"memory": 4096, "num_accelerators": 1, "copies": 1, "num_cpus": 2}
),
inference_component_name=custom_orchestrator_name,
schema_builder=SchemaBuilder(sample_input="Test", sample_output={"generated_text": "test"}),
modelbuilder_list=[llama_model_builder, mistral_mb]
)
```

**Screenshots or logs**
```
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in :4 │
│ │
│ 1 from sagemaker.serve.model_builder import ModelBuilder, SchemaBuilder │
│ 2 from sagemaker.core.inference_config import ResourceRequirements │
│ 3 │
│ ❱ 4 orchestrator = ModelBuilder( │
│ 5 │ inference_spec=SequentialWorkflow( │
│ 6 │ │ region_name=region, │
│ 7 │ │ endpoint_name=endpoint_name, │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: ModelBuilder.__init__() got an unexpected keyword argument 'resource_requirements'
```

```
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in :4 │
│ │
│ 1 from sagemaker.serve.model_builder import ModelBuilder, SchemaBuilder │
│ 2 from sagemaker.core.inference_config import ResourceRequirements │
│ 3 │
│ ❱ 4 orchestrator = ModelBuilder( │
│ 5 │ inference_spec=SequentialWorkflow( │
│ 6 │ │ region_name=region, │
│ 7 │ │ endpoint_name=endpoint_name, │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: ModelBuilder.__init__() got an unexpected keyword argument 'inference_component_name'
```

**Workaround**

````
Setting attributes directly on the instance after construction:

```python
orchestrator = ModelBuilder(
inference_spec=MyOrchestrator(...),
dependencies={"auto": False, "custom": ["cloudpickle"]},
sagemaker_session=sess,
role_arn=role,
schema_builder=SchemaBuilder(sample_input="Test", sample_output={"generated_text": "test"}),
)

# Workaround: set attributes post-construction
orchestrator.resource_requirements = ResourceRequirements(
requests={"memory": 4096, "num_accelerators": 1, "copies": 1, "num_cpus": 2}
)
orchestrator.inference_component_name = "my-orchestrator-ic"

orchestrator.build()
```

This works because `build()` reads `self.resource_requirements` and `self.inference_component_name` via attribute access, but it is undocumented and inconsistent with the published reference sample.

**System information**
A description of your system. Please provide:
- **SageMaker Python SDK version**: `sagemaker-serve 1.20.0 (SDK v3)`
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: sagemaker-distribution-prod:3.2.0-cpu
- **Framework version**:
- **Python version**: 3.12
- **CPU or GPU**: GPU
- **Custom Docker image (Y/N)**: N

**Additional context**
Add any other context about the problem here.

sagemaker-serve/src/sagemaker/serve/model_builder.py で、dataclass ModelBuilder のフィールドと、4507 行目および 4534-4536 行目付近の build() への参照を調べることから始めます。コンストラクターに 2 つのドキュメント化されたパラメーターのサポートを追加し、CustomOrchestrator の例が ResourceRequirements と推論コンポーネント名を指定してビルドされることを確認します。

索引モデルが issue の本文から書いたものです。