WandbLogger: log distributed training experiments (multi-node)

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### Description & Motivation

Currently, when using PyTorch Lightning with `WandbLogger` for multi-node distributed training, **only** the system metrics (CPU/GPU utilization, memory usage, etc.) from **node 0** and logs from **rank 0** are recorded by wandb. This limits visibility into critical performance data from non-zero ranks, making it harder to debug issues like uneven resource utilization across nodes/GPUs or pinpoint hardware bottlenecks in distributed setups.

With wandb's recently added [distributed experiment tracking support](https://docs.wandb.ai/guides/track/log/distributed-training/#track-all-processes-to-a-single-run) (also [the end-to-end example report](https://wandb.ai/dimaduev/simple-cnn-ddp/reports/Distributed-Training-with-Shared-Mode--VmlldzoxMTI0NTE1NA)), it's now possible to collect system metrics and logs from all nodes. However, Lightning's `WandbLogger` does not yet leverage this capability out-of-the-box. This feature request proposes updating Lightning's wandb integration to support full distributed system monitoring.

### Pitch

Add native support in `pytorch_lightning.loggers.WandbLogger` to:

- Log system metrics (hardware telemetry) from ​​all nodes​​ in distributed training, not just node 0

This could be implemented by:

- Adding a `log_all_ranks: bool` parameter to `WandbLogger` to enable/disable this behavior
- `wandb.init(..., settings=wandb.Settings(x_label="rank_0", mode="shared", x_primary=True))` for rank 0
- `wandb.init(..., settings=wandb.Settings(x_label=f"rank_{rank}", mode="shared", x_primary=False))` for non-zero ranks

### Alternatives

While users could manually override logging behavior by modifying wandb.init() parameters in non-zero ranks, this:

- Conflicts with Lightning's logger orchestration
- Risks creating multiple wandb runs unintentionally
- Requires error-prone custom code outside Lightning's abstractions

A cleaner native implementation would provide better safety and usability.

### Additional context

wandb has recently updated the feature, here is the relevant issue:

- https://github.com/wandb/wandb/issues/7470#issuecomment-2814205540

When examining the `WandbLogger` **source code**, the `@rank_zero_experiment` decorator enforces wandb initialization and logging exclusively on rank 0. This creates architectural challenges for multi-rank logging because:

- Non-zero ranks never initialize a `wandb.Run`
- Any attempt to modify the existing logger would require significant refactoring of the decorator logic

Instead of adding a `log_all_ranks` parameter to `WandbLogger`, a cleaner solution could be:

- Create a new `WandbDistributedLogger` class under `lightning.pytorch.loggers.wandb`
- Design this class to:
- Initialize wandb runs on ​​all ranks​​ using `wandb.init(..., settings=wandb.Settings(x_label=..., mode="shared", x_primary=...))`
- Bypass the `@rank_zero_experiment` restriction
- Preserve the original `WandbLogger` behavior (e.g. `log_metrics`, `log_hyperparams`, etc.)

This approach:
- Maintains backward compatibility
- Avoids complicating the existing WandbLogger API

I would be happy to submit a PR implementing if needed.

- The new `WandbDistributedLogger` class
- Integration tests verifying multi-node metric collection

cc @lantiga @borda @morganmcg1 @borisdayma @scottire @parambharat

Read the WandbLogger source in lightning.pytorch.loggers.wandb, especially the @rank_zero_experiment decorator, and review the linked W&B distributed-tracking guidance first. Determine whether the implementation should extend WandbLogger or add WandbDistributedLogger, then use the proposed integration tests to verify all-rank system metrics and preserved logging behavior.

Written by the indexing model from the issue text.