Resuming not correct when `max_steps` corresponds to the end of an epoch

Bug description

The resuming logic for the training loop is not correct in an edge case where max_steps corresponds to the end of an epoch.

What version are you seeing the problem on?

v2.2, master

How to reproduce the bug

import shutil
import time
from pathlib import Path

import torch
from torch.utils.data import DataLoader, Dataset

from pytorch_lightning import LightningModule, Trainer


class RandomDataset(Dataset):
    def __init__(self, size, length):
        self.len = length
        self.data = torch.randn(length, size)

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return self.len


class BoringModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)

    def forward(self, x):
        return self.layer(x)

    def training_step(self, dataloader_iter):
        batch, _, _ = next(dataloader_iter)
        batch, _, _ = next(dataloader_iter)
        loss = self(batch).sum()
        time.sleep(0.5)
        return {"loss": loss}

    def configure_optimizers(self):
        return torch.optim.SGD(self.layer.parameters(), lr=0.1)


train_data = DataLoader(RandomDataset(32, 40), batch_size=2)  # length = 20

checkpoint_dir = Path.cwd() / "checkpoints"
if checkpoint_dir.is_dir():
    shutil.rmtree(checkpoint_dir)


model = BoringModel()
trainer = Trainer(
    default_root_dir=checkpoint_dir,
    max_steps=10,
    # max_epochs=1,
    enable_model_summary=False,
    accelerator="cpu",
)
trainer.fit(model, train_data)

# Resume
trainer = Trainer(
    default_root_dir=checkpoint_dir,
    max_steps=20,
    # max_epochs=2,
    enable_model_summary=False,
    accelerator="cpu",
)
trainer.fit(model, train_data, ckpt_path=checkpoint_dir / "lightning_logs/version_0/checkpoints/epoch=0-step=10.ckpt")

Error messages and logs

The expected behavior is that the loop/progress bar behaves the same when setting max_epochs (commented out above). Instead, when reaching max_steps, the epoch doesn't get bumped and when resuming the checkpoint, we resume in a mid-epoch state.

Environment

#- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow):
#- PyTorch Lightning Version (e.g., 1.5.0):
#- Lightning App Version (e.g., 0.5.2):
#- PyTorch Version (e.g., 2.0):
#- Python version (e.g., 3.9):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
#- Running environment of LightningApp (e.g. local, cloud):

More info

No response

cc @carmocca @justusschock