```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin C"),
    ("x1", "milligrams of vitamin B7")
  ],
  "objective_function": "3.56 * x0 + 9.33 * x1",
  "constraints": [
    "5.67 * x0 + 6.06 * x1 >= 84",
    "0.73 * x0 + 4.16 * x1 >= 21",
    "0.37 * x0 + 1.26 * x1 >= 48",
    "3.56 * x0 + 1.87 * x1 >= 58",
    "5 * x0 + -3 * x1 >= 0",
    "5.67 * x0 + 6.06 * x1 <= 248",
    "0.73 * x0 + 4.16 * x1 <= 90",
    "0.37 * x0 + 1.26 * x1 <= 128",
    "3.56 * x0 + 1.87 * x1 <= 127"
  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    x0 = m.addVar(lb=0, name="vitamin_c")  # milligrams of vitamin C
    x1 = m.addVar(lb=0, name="vitamin_b7") # milligrams of vitamin B7


    # Set objective function
    m.setObjective(3.56 * x0 + 9.33 * x1, gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(5.67 * x0 + 6.06 * x1 >= 84, "c1")
    m.addConstr(0.73 * x0 + 4.16 * x1 >= 21, "c2")
    m.addConstr(0.37 * x0 + 1.26 * x1 >= 48, "c3")
    m.addConstr(3.56 * x0 + 1.87 * x1 >= 58, "c4")
    m.addConstr(5 * x0 - 3 * x1 >= 0, "c5")
    m.addConstr(5.67 * x0 + 6.06 * x1 <= 248, "c6")
    m.addConstr(0.73 * x0 + 4.16 * x1 <= 90, "c7")
    m.addConstr(0.37 * x0 + 1.26 * x1 <= 128, "c8")
    m.addConstr(3.56 * x0 + 1.87 * x1 <= 127, "c9")


    # Optimize model
    m.optimize()

    # Print results
    if m.status == gp.GRB.OPTIMAL:
        print('Optimal objective value: %g' % m.objVal)
        print('milligrams of vitamin C: %g' % x0.x)
        print('milligrams of vitamin B7: %g' % x1.x)
    elif m.status == gp.GRB.INFEASIBLE:
        print('The model 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')
```