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`