AutoModelForSeq2SeqLM is for encoder-decoder models like T5, BART, mBART, PEGASUS. You can for example initialize an AutoModelForSeq2SeqLM from t5-base, like so:
from transformers import AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

EncoderDecoderModel is different, this was created to use encoder-only or decoder-only models in an encoder-decoder setup (this is possible, as was shown in this paper). For example, BERT is an encoder-only model, but you can use it in an encoder-decoder set-up as follows:
from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel

# Initializing a BERT bert-base-uncased style configuration
config_encoder = BertConfig()
config_decoder = BertConfig()

config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)

# Initializing a Bert2Bert model from the bert-base-uncased style configurations
model = EncoderDecoderModel(config=config)

You can also for example use BERT as encoder and GPT-2 as decoder.