Data loss converting from scipy.sparse.csr_matrix to xgboost.DMatrix

scipy csr matrices have an implied value of zero for all non specified values. However this isn't being respected when converting between the two i.e:
```
from scipy.sparse import csr_matrix
from xgboost import DMatrix

raw_data = [
[1, 0, 2],
[0, 3, 0],
[4, 0, 0]
]
sp_csr = csr_matrix(raw_data)
dmatrix = DMatrix(raw_data)

print(dmatrix.get_data())
# Coords Values
# (0, 0) 1.0
# (0, 1) 0.0
# (0, 2) 2.0
# (1, 0) 0.0
# (1, 1) 3.0
# (1, 2) 0.0
# (2, 0) 4.0
# (2, 1) 0.0
# (2, 2) 0.0

print(DMatrix(sp_csr).get_data())
# Coords Values
# (0, 0) 1.0
# (0, 2) 2.0
# (1, 1) 3.0
# (2, 0) 4.0
```
I would expect the two 'print' statements to return equivalent results, but the zeroes are instead being treated as missing values.

Start by reproducing the conversion with scipy.sparse.csr_matrix and DMatrix, then compare get_data() for dense and CSR inputs. Done means explicitly stored zero values are preserved consistently instead of being treated as missing values.

Written by the indexing model from the issue text.