Millimeter-wave (mmWave) extra-large scale multiple-input-multiple-output (XL-MIMO) is a promising technique for achieving high data rates in the upcoming sixth-generation communication networks. This paper considers an indoor massive Internet-of-Things (IoT) access scenario served by mmWave XL-MIMO, where the wireless channels exhibit spatial non-stationarity and the coexistence of far-field and near-field communication. By analyzing and exploiting such mmWave XL-MIMO channels, we propose a low-latency grant-free massive IoT access scheme based on joint active user detection (AUD) and channel estimation (CE). Specifically, by exploiting the common user activity in different pilot subcarriers and the block sparsity of the angular-domain XL-MIMO channels, we propose a low-complexity generalized multiple measurement vector-joint AUD and CE algorithm for efficient indoor massive access. Simulation results verify that the proposed solutions outperform the state-of-the-art greedy compressive sensing-based schemes in terms of AUD and CE performance.