```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin C"),
    ("x1", "milligrams of vitamin A"),
    ("x2", "milligrams of vitamin B7")
  ],
  "objective_function": "3.78 * x0 + 3.66 * x1 + 3.88 * x2",
  "constraints": [
    "2 * x0 + 3 * x1 + 3 * x2 <= 52",  // kidney support index
    "1 * x0 + 4 * x1 + 7 * x2 <= 113", // muscle growth index
    "2 * x0 + 3 * x1 >= 12",
    "4 * x1 + 7 * x2 >= 35",
    "3 * x1 + 3 * x2 <= 48",
    "2 * x0 + 3 * x1 <= 35",
    "2 * x0 + 3 * x1 + 3 * x2 <= 35",
    "1 * x0 + 4 * x1 <= 112",
    "1 * x0 + 7 * x2 <= 79",
    "1 * x0 + 4 * x1 + 7 * x2 <= 79",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0"
  ]
}
```

```python
import gurobipy as gp

try:
    # Create a new model
    m = gp.Model("vitamin_optimization")

    # Create variables
    vitamin_c = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_c")
    vitamin_a = m.addVar(lb=0, vtype=gp.GRB.INTEGER, name="vitamin_a")
    vitamin_b7 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_b7")


    # Set objective function
    m.setObjective(3.78 * vitamin_c + 3.66 * vitamin_a + 3.88 * vitamin_b7, gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(2 * vitamin_c + 3 * vitamin_a + 3 * vitamin_b7 <= 52, "kidney_support_index")
    m.addConstr(vitamin_c + 4 * vitamin_a + 7 * vitamin_b7 <= 113, "muscle_growth_index")
    m.addConstr(2 * vitamin_c + 3 * vitamin_a >= 12, "combined_kidney_support_1")
    m.addConstr(4 * vitamin_a + 7 * vitamin_b7 >= 35, "combined_muscle_growth_1")
    m.addConstr(3 * vitamin_a + 3 * vitamin_b7 <= 48, "combined_kidney_support_2")
    m.addConstr(2 * vitamin_c + 3 * vitamin_a <= 35, "combined_kidney_support_3")
    m.addConstr(2 * vitamin_c + 3 * vitamin_a + 3 * vitamin_b7 <= 35, "combined_kidney_support_4")
    m.addConstr(vitamin_c + 4 * vitamin_a <= 112, "combined_muscle_growth_2")
    m.addConstr(vitamin_c + 7 * vitamin_b7 <= 79, "combined_muscle_growth_3")
    m.addConstr(vitamin_c + 4 * vitamin_a + 7 * vitamin_b7 <= 79, "combined_muscle_growth_4")


    # Optimize model
    m.optimize()

    if m.status == gp.GRB.OPTIMAL:
        print('Obj: %g' % m.objVal)
        print('vitamin_c:', vitamin_c.x)
        print('vitamin_a:', vitamin_a.x)
        print('vitamin_b7:', vitamin_b7.x)

    elif m.status == gp.GRB.INFEASIBLE:
        print('Optimization problem is infeasible.')
    else:
        print('Optimization ended with status %d' % m.status)


except gp.GurobiError as e:
    print('Error code ' + str(e.errno) + ': ' + str(e))

except AttributeError:
    print('Encountered an attribute error')
```