The magnitude doesn't say anything about the path to the optimum, in fact nothing in gradient decent knows anything about the optimum and the algorithm relays on local information only. However if you assume that the space of available solutions is relatively smooth and doesn't contain sharp drops, you can treat the magnitude of the gradient as a score of your confidence in your current surrounding.
So if the gradient magnitude is high, you are fairly certain that you are far from a good solution and you can make big steps and if the magnitude is small, than you are closer to a good solution and should make smaller changes.
The learning rate is just a hyper-parameter that controls how much we are adjusting the weights of our network with respect that said loss gradient. It can also be viewed in terms of your confidence in the surrounding solutions space. It also helps the model to converge deeper into a specific minimum point (as we reduce the learning rate during training).