Distributed mode overwrites the user's choice for dataloaders shuffling

Nobody has claimed this yet.

## 🐛 Bug
I ordered my training data in a specific manner and passed it to the DataLoader with `shuffle=False` (I use `reload_dataloaders_every_n_epochs=1` to control it every epoch). Then, I found out that running in distributed mode re-creates the train dataloader and always set `shuffle=True`. The opposite happens for the val dataloader.

I think that the re-creation of the dataloaders must be using the same shuffle setting the original dataloaders had.
Is there any reason not to do so?

---
Here is the related code with the hard-coded shuffle values:

train - shuffle forced to be true
https://github.com/PyTorchLightning/pytorch-lightning/blob/c33df2639f19d49c5e7520294e3221efe402d684/pytorch_lightning/trainer/data_loading.py#L330-L333

val - shuffle forced to be true
https://github.com/PyTorchLightning/pytorch-lightning/blob/c33df2639f19d49c5e7520294e3221efe402d684/pytorch_lightning/trainer/data_loading.py#L442-L443

cc @borda @justusschock @awaelchli

Start in pytorch_lightning/trainer/data_loading.py at the linked train-loader lines 330-333 and validation-loader lines 442-443. Trace how distributed mode recreates each DataLoader and preserve the original shuffle choices for both loaders. Done means user-specified train and validation shuffling remains unchanged in distributed mode, with the relevant dataloader tests passing.

Written by the indexing model from the issue text.