We present ExoMiner++, an enhanced deep learning model that builds on the success of ExoMiner to improve transit signal classification in 2-minute TESS data. ExoMiner++ incorporates additional diagnostic inputs, including periodogram, flux trend, difference image, unfolded flux, and spacecraft attitude control data, all of which are crucial for effectively distinguishing transit signals from more challenging sources of false positives (FPs). To further enhance performance, we leverage multisource training by combining high-quality labeled data from the Kepler space telescope with TESS data. This approach mitigates the impact of TESS's noisier and more ambiguous labels. ExoMiner++ achieves high accuracy across various classification and ranking metrics, significantly narrowing the search space for follow-up investigations to confirm new planets. To serve the exoplanet community, we introduce a new TESS catalog containing ExoMiner++ classifications and confidence scores for each transit signal. Among the 147,568 unlabeled TCEs, ExoMiner++ identifies 7330 as planet candidates (PCs), with the remainder classified as FPs. These 7330 PCs correspond to 1868 existing TESS Objects of Interest (TOIs), 69 Community TESS Objects of Interest (CTOIs), and 50 newly introduced CTOIs. 1797 out of the 2506 TOIs previously labeled as PCs in ExoFOP are classified as PCs by ExoMiner++. This reduction in plausible candidates, combined with the excellent ranking quality of ExoMiner++, allows the follow-up efforts to be focused on the most likely candidates, increasing the overall planet yield.