sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0

## Describe the feature you'd like

`sagemaker-train` (and `sagemaker-serve`) declare an unconditional dependency on the full `mlflow` distribution:

```
sagemaker-train 1.18.0: mlflow<4.0.0,>=3.0.0
```

The training integration uses MLflow purely as a tracking client (logging runs and metrics to a configured tracking server), which `mlflow-skinny` implements completely. The full distribution additionally drags in the tracking server stack and its dependency constraints.

`sagemaker-mlflow` already made exactly this change in 0.5.0: `mlflow-skinny>=2.8` as the base requirement, with full mlflow relegated to an optional `full` extra. Applying the same pattern to `sagemaker-train` and `sagemaker-serve` would make the SDK family consistent.

## Why it matters

Full mlflow currently caps `cryptography<50`. Snyk advisories SNYK-PYTHON-CRYPTOGRAPHY-18516620/21/22 (two high severity) are fixed only in cryptography 50.0.0, so any project consuming sagemaker-train transitively cannot reach the fixed version and must either waive the findings or remove sagemaker-train. Since the mlflow usage is client-only, the cap buys nothing for these packages.

## Suggested change

Mirror sagemaker-mlflow 0.5.0: depend on `mlflow-skinny`, offer full mlflow behind an extra for anyone who genuinely needs the server components.

检查 sagemaker-train 和 sagemaker-serve 的依赖元数据，然后将其与 issue 中描述的 sagemaker-mlflow 0.5.0 模式进行比较。验证 mlflow-skinny 是基础要求，完整的 mlflow 可通过可选 extra 提供，并且依赖解析不再强制施加所报告的 cryptography 上限。

由索引模型根据 Issue 内容生成。