## Problem Description and Symbolic Representation

The problem requires maximizing the objective function:

1.94 * (milligrams of vitamin B7) + 6.5 * (milligrams of iron)

subject to the following constraints:

* The kidney support index for milligrams of vitamin B7 is 13.
* The kidney support index of milligrams of iron is 11.
* The total combined kidney support index from milligrams of vitamin B7 plus milligrams of iron should be as much or more than 37.
* -10 * (milligrams of vitamin B7) + 2 * (milligrams of iron) must be greater than or equal to zero.
* The total combined kidney support index from milligrams of vitamin B7, and milligrams of iron must be equal to or less than 95.
* You are allowed to have a non-integer number of milligrams of vitamin B7.
* You must have a nonfractional number of milligrams of iron.

## Symbolic Representation

```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B7'), 
        ('x2', 'milligrams of iron')
    ], 
    'objective_function': '1.94 * x1 + 6.5 * x2', 
    'constraints': [
        '13 * x1 <= 114', 
        '11 * x2 <= 114', 
        '13 * x1 + 11 * x2 >= 37', 
        '-10 * x1 + 2 * x2 >= 0', 
        '13 * x1 + 11 * x2 <= 95'
    ]
}
```

## Gurobi Code

```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name='milligrams of vitamin B7', lb=0, ub=None, integrality=0)
x2 = model.addVar(name='milligrams of iron', lb=0, ub=None, integrality=1)

# Set the objective function
model.setObjective(1.94 * x1 + 6.5 * x2, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(13 * x1 <= 114, name='kidney_support_index_vitamin_B7')
model.addConstr(11 * x2 <= 114, name='kidney_support_index_iron')
model.addConstr(13 * x1 + 11 * x2 >= 37, name='combined_kidney_support_index_min')
model.addConstr(-10 * x1 + 2 * x2 >= 0, name='linear_constraint')
model.addConstr(13 * x1 + 11 * x2 <= 95, name='combined_kidney_support_index_max')

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Optimal solution found.')
    print(f'Milligrams of vitamin B7: {x1.varValue}')
    print(f'Milligrams of iron: {x2.varValue}')
    print(f'Objective function value: {model.objVal}')
else:
    print('No optimal solution found.')
```