```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin B4"),
    ("x1", "milligrams of vitamin B6"),
    ("x2", "milligrams of vitamin B12"),
    ("x3", "milligrams of vitamin B2")
  ],
  "objective_function": "2*x0 + 9*x1 + 9*x2 + 9*x3",
  "constraints": [
    "12.0*x0 + 11.55*x3 >= 51",
    "14.36*x1 + 12.39*x2 >= 41",
    "12.0*x0 + 12.39*x2 + 11.55*x3 >= 72",
    "12.0*x0 + 14.36*x1 + 12.39*x2 >= 72",
    "14.36*x1 + 12.39*x2 + 11.55*x3 >= 72",
    "12.0*x0 + 12.39*x2 + 11.55*x3 >= 52",
    "12.0*x0 + 14.36*x1 + 12.39*x2 >= 52",
    "14.36*x1 + 12.39*x2 + 11.55*x3 >= 52",
    "12.0*x0 + 12.39*x2 + 11.55*x3 >= 80",
    "12.0*x0 + 14.36*x1 + 12.39*x2 >= 80",
    "14.36*x1 + 12.39*x2 + 11.55*x3 >= 80",
    "0.68*x1 + 10.72*x3 >= 44",
    "6.74*x0 + 3.27*x2 >= 43",
    "0.02*x1 + 4.19*x2 >= 19",
    "4.16*x0 + 0.02*x1 >= 42",
    "4.16*x0 + 4.19*x2 >= 48",
    "4.16*x0 + 11.26*x3 >= 31",
    "4.19*x2 + 11.26*x3 >= 41",
    "12.0*x0 + 11.55*x3 <= 272",
    "12.39*x2 + 11.55*x3 <= 319",
    "14.36*x1 + 11.55*x3 <= 119",
    "12.0*x0 + 12.39*x2 <= 228",
    "12.0*x0 + 14.36*x1 + 12.39*x2 + 11.55*x3 <= 228",
    "6.74*x0 + 0.68*x1 <= 186",
    "6.74*x0 + 3.27*x2 <= 121",
    "3.27*x2 + 10.72*x3 <= 136",
    "0.68*x1 + 3.27*x2 <= 127",
    "6.74*x0 + 3.27*x2 + 10.72*x3 <= 117",
    "0.68*x1 + 3.27*x2 + 10.72*x3 <= 168",
    "6.74*x0 + 0.68*x1 + 3.27*x2 + 10.72*x3 <= 168",
    "3.54*x2 + 14.05*x3 <= 94",
    "5.9*x1 + 3.54*x2 <= 64",
    "13.06*x0 + 5.9*x1 <= 38",
    "13.06*x0 + 14.05*x3 <= 118",
    "5.9*x1 + 14.05*x3 <= 64",
    "13.06*x0 + 5.9*x1 + 3.54*x2 + 14.05*x3 <= 64",
    "4.16*x0 + 11.26*x3 <= 119",
    "0.02*x1 + 4.19*x2 <= 124",
    "4.16*x0 + 0.02*x1 + 4.19*x2 + 11.26*x3 <= 124",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0",
    "x3 >= 0"
  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    x = m.addVars(4, lb=0, vtype=gp.GRB.CONTINUOUS, names=["x0", "x1", "x2", "x3"])


    # Set objective function
    m.setObjective(2*x[0] + 9*x[1] + 9*x[2] + 9*x[3], gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(12.0*x[0] + 11.55*x[3] >= 51)
    m.addConstr(14.36*x[1] + 12.39*x[2] >= 41)
    m.addConstr(12.0*x[0] + 12.39*x[2] + 11.55*x[3] >= 72)
    m.addConstr(12.0*x[0] + 14.36*x[1] + 12.39*x[2] >= 72)
    m.addConstr(14.36*x[1] + 12.39*x[2] + 11.55*x[3] >= 72)
    m.addConstr(12.0*x[0] + 12.39*x[2] + 11.55*x[3] >= 52)
    m.addConstr(12.0*x[0] + 14.36*x[1] + 12.39*x[2] >= 52)
    m.addConstr(14.36*x[1] + 12.39*x[2] + 11.55*x[3] >= 52)
    m.addConstr(12.0*x[0] + 12.39*x[2] + 11.55*x[3] >= 80)
    m.addConstr(12.0*x[0] + 14.36*x[1] + 12.39*x[2] >= 80)
    m.addConstr(14.36*x[1] + 12.39*x[2] + 11.55*x[3] >= 80)
    m.addConstr(0.68*x[1] + 10.72*x[3] >= 44)
    m.addConstr(6.74*x[0] + 3.27*x[2] >= 43)
    m.addConstr(0.02*x[1] + 4.19*x[2] >= 19)
    m.addConstr(4.16*x[0] + 0.02*x[1] >= 42)
    m.addConstr(4.16*x[0] + 4.19*x[2] >= 48)
    m.addConstr(4.16*x[0] + 11.26*x[3] >= 31)
    m.addConstr(4.19*x[2] + 11.26*x[3] >= 41)
    m.addConstr(12.0*x[0] + 11.55*x[3] <= 272)
    m.addConstr(12.39*x[2] + 11.55*x[3] <= 319)
    m.addConstr(14.36*x[1] + 11.55*x[3] <= 119)
    m.addConstr(12.0*x[0] + 12.39*x[2] <= 228)
    m.addConstr(12.0*x[0] + 14.36*x[1] + 12.39*x[2] + 11.55*x[3] <= 228)
    m.addConstr(6.74*x[0] + 0.68*x[1] <= 186)
    m.addConstr(6.74*x[0] + 3.27*x[2] <= 121)
    m.addConstr(3.27*x[2] + 10.72*x[3] <= 136)
    m.addConstr(0.68*x[1] + 3.27*x[2] <= 127)
    m.addConstr(6.74*x[0] + 3.27*x[2] + 10.72*x[3] <= 117)
    m.addConstr(0.68*x[1] + 3.27*x[2] + 10.72*x[3] <= 168)
    m.addConstr(6.74*x[0] + 0.68*x[1] + 3.27*x[2] + 10.72*x[3] <= 168)
    m.addConstr(3.54*x[2] + 14.05*x[3] <= 94)
    m.addConstr(5.9*x[1] + 3.54*x[2] <= 64)
    m.addConstr(13.06*x[0] + 5.9*x[1] <= 38)
    m.addConstr(13.06*x[0] + 14.05*x[3] <= 118)
    m.addConstr(5.9*x[1] + 14.05*x[3] <= 64)
    m.addConstr(13.06*x[0] + 5.9*x[1] + 3.54*x[2] + 14.05*x[3] <= 64)
    m.addConstr(4.16*x[0] + 11.26*x[3] <= 119)
    m.addConstr(0.02*x[1] + 4.19*x[2] <= 124)
    m.addConstr(4.16*x[0] + 0.02*x[1] + 4.19*x[2] + 11.26*x[3] <= 124)


    # Optimize model
    m.optimize()

    if m.status == gp.GRB.OPTIMAL:
        for v in m.getVars():
            print(f'{v.varName}: {v.x}')
        print(f'Objective Value: {m.objVal}')
    elif m.status == gp.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print(f"Optimization ended with status {m.status}")


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

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