High Level Understanding of Neural Machine Translation
In encoder-decoder architecture, we first represent the input sequence by a fixed vector. It is assumed that this fix vector represents the complete meaning of the sentence. Now decoder uses this fix vector to generate the output sequence. 

Answers of your questions:


If you use 1 recurrent unit, it outputs the value at each time instant which is not suitable for machine translation as translation of first word may be in the last or somewhere else in target language.(I am not sure how will you map the output and input in that case.)
Regarding synonyms, system can handle synonyms as words which are synonyms have very close word vectors.
Regarding Word-Order, it can learn the word order if you have enough training data. Decoder also uses beam search to find the best possible translation. I will suggest you to read this paper thoroughly.
https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf
Regarding evaluation, there are various metrics which are designed to evaluate the machine translation system. BLEU is one of the most famous one out of all. It uses various reference translations. You can read more about BLEU in the paper by Kishore Papineni et. al.(I can not post more than two link in answer as my reputation is less than 10.)


I will also suggest you to read the below paper regarding neural machine translation.
https://arxiv.org/pdf/1409.0473.pdf

Hope it helps!!