Downloading artifacts with wandblogger in DDP case failing on non-zero rank processes

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### Bug description

In case using Wandblogger `download_artifact` function in a DDP case with multiple GPUs - the artifact won't be downloaded in other processes beside the 0-rank process. The function wrapped with the decorator `rank_zero_only` and thus returning `None` and not executing the method.

### What version are you seeing the problem on?

v2.1

### How to reproduce the bug

```python
from lightning.pytorch.loggers import WandbLogger
import lightning as L
import os
from torchvision.transforms import ToTensor
from torchvision.datasets import MNIST
from torch import utils

logger = WandbLogger()

artifact_path = logger.download_artifact()

trainer = L.Trainer(logger=logger, accelerator='gpu', devices=[0,1,2])

dataset = MNIST(os.getcwd(), download=True, transform=ToTensor())
train_dataloader = utils.data.DataLoader(dataset)

model = MyLightningModule()
trainer.fit(model, train_dataloader)

# The output on the 0-rank process will be the real path but in other processes will be None
print(artifact_path)
```

### Error messages and logs

```
# Error messages and logs here please
```

### Environment

Current environment

* CUDA:
- GPU:
- NVIDIA A10G
- NVIDIA A10G
- NVIDIA A10G
- NVIDIA A10G
- available: True
- version: 12.1
* Lightning:
- lightning: 2.1.0
- lightning-utilities: 0.9.0
- pytorch-lightning: 2.1.0
- torch: 2.1.0
- torchmetrics: 1.2.0
- torchvision: 0.16.0
* Packages:
- aiohttp: 3.8.6
- aiosignal: 1.3.1
- annotated-types: 0.6.0
- anyio: 4.0.0
- appdirs: 1.4.4
- argon2-cffi: 23.1.0
- argon2-cffi-bindings: 21.2.0
- arrow: 1.3.0
- asttokens: 2.4.1
- async-lru: 2.0.4
- async-timeout: 4.0.3
- attrs: 23.1.0
- babel: 2.13.1
- beautifulsoup4: 4.12.2
- bleach: 6.1.0
- bokeh: 3.3.0
- boto3: 1.28.79
- botocore: 1.31.79
- certifi: 2023.7.22
- cffi: 1.16.0
- charset-normalizer: 3.3.2
- click: 8.1.7
- colorcet: 3.0.1
- comm: 0.2.0
- compress-pickle: 2.1.0
- contourpy: 1.2.0
- cython: 0.29.36
- datasets: 2.14.6
- debugpy: 1.8.0
- decorator: 5.1.1
- defusedxml: 0.7.1
- dill: 0.3.7
- docker-pycreds: 0.4.0
- executing: 2.0.1
- fastjsonschema: 2.18.1
- filelock: 3.13.1
- fqdn: 1.5.1
- frozenlist: 1.4.0
- fsspec: 2023.10.0
- gitdb: 4.0.11
- gitpython: 3.1.40
- hdbscan: 0.8.33
- holoviews: 1.18.0
- huggingface-hub: 0.17.3
- idna: 3.4
- ipykernel: 6.26.0
- ipython: 8.17.2
- isoduration: 20.11.0
- jedi: 0.19.1
- jinja2: 3.1.2
- jmespath: 1.0.1
- joblib: 1.3.2
- json5: 0.9.14
- jsonpointer: 2.4
- jsonschema: 4.19.2
- jsonschema-specifications: 2023.7.1
- jupyter-client: 8.6.0
- jupyter-core: 5.5.0
- jupyter-events: 0.9.0
- jupyter-lsp: 2.2.0
- jupyter-server: 2.10.0
- jupyter-server-terminals: 0.4.4
- jupyterlab: 4.0.8
- jupyterlab-pygments: 0.2.2
- jupyterlab-server: 2.25.0
- lightning: 2.1.0
- lightning-utilities: 0.9.0
- linkify-it-py: 2.0.2
- llvmlite: 0.41.1
- lz4: 4.3.2
- markdown: 3.5.1
- markdown-it-py: 3.0.0
- markupsafe: 2.1.3
- matplotlib-inline: 0.1.6
- mdit-py-plugins: 0.4.0
- mdurl: 0.1.2
- mistune: 3.0.2
- mpmath: 1.3.0
- multidict: 6.0.4
- multiprocess: 0.70.15
- nbclient: 0.9.0
- nbconvert: 7.11.0
- nbformat: 5.9.2
- nest-asyncio: 1.5.8
- networkx: 3.2.1
- nltk: 3.8.1
- notebook: 7.0.6
- notebook-shim: 0.2.3
- numba: 0.58.1
- numpy: 1.26.1
- nvidia-cublas-cu12: 12.1.3.1
- nvidia-cuda-cupti-cu12: 12.1.105
- nvidia-cuda-nvrtc-cu12: 12.1.105
- nvidia-cuda-runtime-cu12: 12.1.105
- nvidia-cudnn-cu12: 8.9.2.26
- nvidia-cufft-cu12: 11.0.2.54
- nvidia-curand-cu12: 10.3.2.106
- nvidia-cusolver-cu12: 11.4.5.107
- nvidia-cusparse-cu12: 12.1.0.106
- nvidia-nccl-cu12: 2.18.1
- nvidia-nvjitlink-cu12: 12.3.52
- nvidia-nvtx-cu12: 12.1.105
- overrides: 7.4.0
- packaging: 23.2
- pandas: 2.1.2
- pandocfilters: 1.5.0
- panel: 1.3.1
- param: 2.0.0
- parso: 0.8.3
- pexpect: 4.8.0
- pillow: 10.1.0
- pip: 23.3
- platformdirs: 3.11.0
- prometheus-client: 0.18.0
- prompt-toolkit: 3.0.39
- protobuf: 4.25.0
- psutil: 5.9.6
- ptyprocess: 0.7.0
- pure-eval: 0.2.2
- pyarrow: 14.0.0
- pycparser: 2.21
- pyct: 0.5.0
- pydantic: 2.4.2
- pydantic-core: 2.10.1
- pydantic-numpy: 4.0.0
- pygments: 2.16.1
- pynndescent: 0.5.10
- python-dateutil: 2.8.2
- python-json-logger: 2.0.7
- pytorch-lightning: 2.1.0
- pytz: 2023.3.post1
- pyviz-comms: 3.0.0
- pyyaml: 6.0.1
- pyzmq: 25.1.1
- quiver: 0.0.2
- referencing: 0.30.2
- regex: 2023.10.3
- requests: 2.31.0
- requests-file: 1.5.1
- rfc3339-validator: 0.1.4
- rfc3986-validator: 0.1.1
- rpds-py: 0.12.0
- ruamel.yaml: 0.17.40
- ruamel.yaml.clib: 0.2.8
- s3transfer: 0.7.0
- safetensors: 0.4.0
- scikit-learn: 1.3.2
- scipy: 1.11.3
- semver: 3.0.2
- send2trash: 1.8.2
- sentence-transformers: 2.2.2
- sentencepiece: 0.1.99
- sentry-sdk: 1.34.0
- setproctitle: 1.3.3
- setuptools: 68.0.0
- six: 1.16.0
- smmap: 5.0.1
- sniffio: 1.3.0
- soupsieve: 2.5
- stack-data: 0.6.3
- sympy: 1.12
- tbb: 2021.10.0
- terminado: 0.17.1
- threadpoolctl: 3.2.0
- tinycss2: 1.2.1
- tldextract: 5.1.0
- tokenizers: 0.14.1
- torch: 2.1.0
- torchmetrics: 1.2.0
- torchvision: 0.16.0
- tornado: 6.3.3
- tqdm: 4.66.1
- traitlets: 5.13.0
- transformers: 4.35.0
- triton: 2.1.0
- types-python-dateutil: 2.8.19.14
- typing-extensions: 4.8.0
- tzdata: 2023.3
- uc-micro-py: 1.0.2
- umap-learn: 0.5.4
- uri-template: 1.3.0
- urllib3: 2.0.7
- wandb: 0.16.0
- wcwidth: 0.2.9
- webcolors: 1.13
- webencodings: 0.5.1
- websocket-client: 1.6.4
- wheel: 0.41.2
- xgboost: 2.0.1
- xxhash: 3.4.1
- xyzservices: 2023.10.1
- yarl: 1.9.2
* System:
- OS: Linux
- architecture:
- 64bit
- ELF
- processor: x86_64
- python: 3.11.5
- release: 6.2.0-1015-aws
- version: #15~22.04.1-Ubuntu SMP Fri Oct 6 21:37:24 UTC 2023

### More info

_No response_

cc @awaelchli @morganmcg1 @borisdayma @scottire @parambharat

Start at WandbLogger.download_artifact and inspect its rank_zero_only decoration in the logger implementation. Reproduce the issue with the provided multi-GPU DDP example and verify the behavior on rank 0 and non-zero ranks. Done means artifact downloading works consistently across processes and the returned path is usable on every rank.

Written by the indexing model from the issue text.