For a graduate level, but highly accessible, treatment of econometrics, I would recommend Hayashi - Econometrics. It covers some of the topics you mentioned in more detail, and also dives into the (probability) theory behind them. The chapters on GMM and the asymptotic theory of M-estimators are my personal highlights.
For a more practically oriented, highly readable and modern treatment of econometrics from the perspective of causal inference, I recommend Cunningham - Causal Inference: the mixtape. It covers diff-in-diff and regression discontinuity, but also several other methods for estimating causal parameters. It also places these models in either the context of directed acyclical graphs or the potential outcomes framework, so you get a better view of how researchers talk and think about causality.
Finally, if you are interested in time series, I recommend Brockwell and Davis - Introduction to time series and forecasting. This is indeed an introduction, but (depending on your math background) it will take work to understand the material. If you spend some time with it, it pays a lot of dividends in terms of developing a deep understanding of the topic.