ModelBuilder.deploy() should expose DataCacheConfig and other CreateInferenceComponent API parameters

**Describe the feature you'd like**

Expose additional `CreateInferenceComponent` API parameters through `ModelBuilder.deploy()` when deploying Inference Components. Currently, `ModelBuilder._deploy_core_endpoint()` builds a minimal `InferenceComponentSpecification` and hardcodes several values. The following API-supported configuration is not surfaced:

- **`Specification.DataCacheConfig.EnableCaching`** — Cache model artifacts and container images on instances for faster auto-scaling cold starts
- **`Specification.BaseInferenceComponentName`** — Adapter component deployment (e.g. LoRA adapters attached to a base model)
- **`Specification.Container`** (`Image`, `ArtifactUrl`, `Environment`) — Custom container images, artifact URLs, and environment variables at the IC level
- **`VariantName`** (top-level) — Currently hardcoded to `"AllTraffic"`, not configurable for multi-variant endpoints

**How would this feature be used? Please describe.**

Our immediate need is `DataCacheConfig.EnableCaching`. When deploying large model artifacts (700MB+ Triton ensembles) on auto-scaling endpoints, new instances must re-download all artifacts during scale-out. Enabling caching eliminates this overhead.

Ideally these would be optional parameters on `deploy()` or on `ResourceRequirements`:

```python
builder.deploy(
endpoint_name="my-endpoint",
inference_component_name="my-ic",
instance_type="ml.g5.2xlarge",
initial_instance_count=1,
inference_config=ResourceRequirements(
requests={"memory": 8192, "num_accelerators": 1, "num_cpus": 2, "copies": 1}
),
data_cache_config={"enable_caching": True}, # new
)
```

**Describe alternatives you've considered**

The current workaround is to use `ModelBuilder.build()` for model creation, then call `EndpointConfig.create()`, `Endpoint.create()`, and `InferenceComponent.create()` directly via sagemaker-core, which supports the full `InferenceComponentSpecification`:

```python
from sagemaker.core.resources import EndpointConfig, Endpoint, InferenceComponent
from sagemaker.core.shapes import (
ProductionVariant,
InferenceComponentSpecification,
InferenceComponentComputeResourceRequirements,
InferenceComponentRuntimeConfig,
InferenceComponentDataCacheConfig,
)

builder.build(model_name=sm_model_name)

EndpointConfig.create(
endpoint_config_name=epc_name,
production_variants=[
ProductionVariant(
variant_name="AllTraffic",
instance_type="ml.g5.2xlarge",
initial_instance_count=1,
)
],
execution_role_arn=role,
)

Endpoint.create(
endpoint_name=endpoint_name,
endpoint_config_name=epc_name,
)

InferenceComponent.create(
inference_component_name=ic_name,
endpoint_name=endpoint_name,
variant_name="AllTraffic",
specification=InferenceComponentSpecification(
model_name=sm_model_name,
compute_resource_requirements=InferenceComponentComputeResourceRequirements(
min_memory_required_in_mb=8192,
number_of_accelerator_devices_required=1,
number_of_cpu_cores_required=2,
),
data_cache_config=InferenceComponentDataCacheConfig(enable_caching=True),
),
runtime_config=InferenceComponentRuntimeConfig(copy_count=1),
)
```

This works but defeats the purpose of `ModelBuilder.deploy()` as a high-level abstraction. Customers shouldn't need to drop down to sagemaker-core or boto3 for commonly used API parameters.

**Additional context**

- API reference: [CreateInferenceComponent](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateInferenceComponent.html)
- The shape classes (`InferenceComponentDataCacheConfig`, `InferenceComponentContainerSpecification`, etc.) already exist in `sagemaker.core.shapes` — they just need to be wired into `ModelBuilder._deploy_core_endpoint()`.
- SageMaker SDK version: `3.x`

Beginnen Sie in ModelBuilder.deploy() und ModelBuilder._deploy_core_endpoint(), und untersuchen Sie anschließend die vorhandenen InferenceComponentSpecification- und verwandten Shape-Klassen in sagemaker.core.shapes. Verfolgen Sie, wie die Parameter für CreateInferenceComponent zusammengesetzt werden, und identifizieren Sie die vorhandenen Deployment-Tests des Projekts. Die Aufgabe ist abgeschlossen, wenn optionale Einstellungen für data cache, base component, container und variant durchgereicht werden können, anstatt weggelassen oder fest codiert zu werden.

Vom Indexierungsmodell aus dem Issue-Text verfasst.