Dear @kumareshbabuns,
Dummy variables and embeddings (or word embeddings) are two different things. Both are vector representations for categorical variables. The former is a sparse representation where only one of the values of each vector representation is 1 rest being 0.  'Embeddings" are a dense vector representation for categorical variables or words, learned using some other neural network.
One way to understand this in practice could be, say you have 3 categorical variables - a, b and c in your features. Using one hot encoding or pandas dummies you can get a vector representation for a,b, c as [1,0,0] , [0,1,0] , [0,0,1] respectively. Since, there are 3 categories, the size of the one hot encoded vectors is equal to 3, with one value being 1. Imagine you have 10000 unique categorical features, each category will then have to be represented using a vector of size 10000 (hence, sparse).
In this case using ‘Embeddings’ could be useful, as you can then learn a dense vector representation (after adequate training) of any size, for each categorical feature.  This is particularly the case while dealing with text data as usually high number of unique words exists in a corpus. Therefore, using pre-trained word embeddings or training word embeddings on the fly (using embedding layer) is useful. Remember, to do this you will need to feed the network with a lot of text which contains the words that exist in the dataset. For e.g. to get a general representation of the word ‘sun’ you will need a lot of sentences that contain the word ‘sun’.
In a scenario where the input is a mix of categorical and numerical features, one can either use pre trained word embeddings for categories and concatenate them with the numerical features. E.g. if one input sample is -> [1,20, a, b], you can either pre process the input and replace ‘a’ and ‘b’ with the pandas dummies. In this case the input will look like - [1, 20, 1,0 , 0,1] (considering only ‘a’ and ‘b’ exists).
OR
Use an embedding layer for processing ‘a’ and ‘b’ part of the input and concatenate the output of embedding layer with [1,20]. Therefore the input become - [1, 20, <embedding for a>, <embedding for b>]
Hope this helps.
Thank you