```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin D"),
    ("x1", "milligrams of vitamin B7"),
    ("x2", "milligrams of magnesium"),
    ("x3", "milligrams of zinc"),
    ("x4", "milligrams of vitamin B2")
  ],
  "objective_function": "5.67 * x0 + 8.95 * x1 + 7.6 * x2 + 4.9 * x3 + 4.43 * x4",
  "constraints": [
    "x1 + x3 >= 9",
    "x1 + x2 + x3 >= 6",
    "x1 + x3 + x4 >= 6",
    "x0 + x3 + x4 >= 6",
    "x1 + x2 + x4 >= 6",
    "x1 + x2 + x3 >= 8",
    "x1 + x3 + x4 >= 8",
    "x0 + x3 + x4 >= 8",
    "x1 + x2 + x4 >= 8",
    "x1 + x2 + x3 >= 6",
    "x1 + x3 + x4 >= 6",
    "x0 + x3 + x4 >= 6",
    "x1 + x2 + x4 >= 6",
    "x1 + x2 + x3 >= 12",
    "x1 + x3 + x4 >= 12",
    "x0 + x3 + x4 >= 12",
    "x1 + x2 + x4 >= 12",
    "x3 + x4 <= 58",
    "x2 + x3 <= 54",
    "x0 + x3 <= 31",
    "x0 + x2 <= 41",
    "x1 + x2 <= 48",
    "x1 + x2 + x4 <= 35",
    "x0 + x1 + x2 <= 44",
    "x0 + x1 + x4 <= 50",
    "x2 + x3 + x4 <= 45",
    "x1 + x3 + x4 <= 33",
    "x0 + x1 + x2 + x3 + x4 <= 33",
    "1*x0 + 4*x1 + 8*x2 + 1*x3 + 1*x4 <= 62" 
  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    vitamin_d = m.addVar(vtype=gp.GRB.CONTINUOUS, name="vitamin_d")
    vitamin_b7 = m.addVar(vtype=gp.GRB.INTEGER, name="vitamin_b7")
    magnesium = m.addVar(vtype=gp.GRB.CONTINUOUS, name="magnesium")
    zinc = m.addVar(vtype=gp.GRB.INTEGER, name="zinc")
    vitamin_b2 = m.addVar(vtype=gp.GRB.INTEGER, name="vitamin_b2")


    # Set objective function
    m.setObjective(5.67 * vitamin_d + 8.95 * vitamin_b7 + 7.6 * magnesium + 4.9 * zinc + 4.43 * vitamin_b2, gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(vitamin_b7 + zinc >= 9)
    m.addConstr(vitamin_b7 + magnesium + zinc >= 6)
    m.addConstr(vitamin_b7 + zinc + vitamin_b2 >= 6)
    m.addConstr(vitamin_d + zinc + vitamin_b2 >= 6)
    m.addConstr(vitamin_b7 + magnesium + vitamin_b2 >= 6)
    m.addConstr(vitamin_b7 + magnesium + zinc >= 8)
    m.addConstr(vitamin_b7 + zinc + vitamin_b2 >= 8)
    m.addConstr(vitamin_d + zinc + vitamin_b2 >= 8)
    m.addConstr(vitamin_b7 + magnesium + vitamin_b2 >= 8)
    m.addConstr(vitamin_b7 + magnesium + zinc >= 12)
    m.addConstr(vitamin_b7 + zinc + vitamin_b2 >= 12)
    m.addConstr(vitamin_d + zinc + vitamin_b2 >= 12)
    m.addConstr(vitamin_b7 + magnesium + vitamin_b2 >= 12)
    m.addConstr(zinc + vitamin_b2 <= 58)
    m.addConstr(magnesium + zinc <= 54)
    m.addConstr(vitamin_d + zinc <= 31)
    m.addConstr(vitamin_d + magnesium <= 41)
    m.addConstr(vitamin_b7 + magnesium <= 48)
    m.addConstr(vitamin_b7 + magnesium + vitamin_b2 <= 35)
    m.addConstr(vitamin_d + vitamin_b7 + magnesium <= 44)
    m.addConstr(vitamin_d + vitamin_b7 + vitamin_b2 <= 50)
    m.addConstr(magnesium + zinc + vitamin_b2 <= 45)
    m.addConstr(vitamin_b7 + zinc + vitamin_b2 <= 33)
    m.addConstr(vitamin_d + vitamin_b7 + magnesium + zinc + vitamin_b2 <= 33)
    m.addConstr(1*vitamin_d + 4*vitamin_b7 + 8*magnesium + 1*zinc + 1*vitamin_b2 <= 62)


    # Optimize model
    m.optimize()

    if m.status == gp.GRB.OPTIMAL:
        print('Obj: %g' % m.objVal)
        for v in m.getVars():
            print('%s %g' % (v.varName, v.x))
    elif m.status == gp.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model could not be solved to optimality.")


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

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