```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin B1"),
    ("x1", "milligrams of vitamin B5"),
    ("x2", "milligrams of vitamin B3"),
    ("x3", "milligrams of vitamin B2"),
    ("x4", "milligrams of potassium")
  ],
  "objective_function": "2*x0 + 3*x1 + 3*x2 + 5*x3 + 9*x4",
  "constraints": [
    "13*x0 + 21*x1 >= 46",
    "23*x2 + 16*x3 >= 37",
    "21*x1 + 16*x3 >= 33",
    "13*x0 + 23*x2 >= 51",
    "21*x1 + 23*x2 >= 59",
    "16*x3 + 9*x4 >= 33",
    "13*x0 + 9*x4 >= 53",
    "13*x0 + 23*x2 + 16*x3 >= 45",
    "13*x0 + 23*x2 + 9*x4 >= 45",
    "21*x1 + 16*x3 + 9*x4 >= 45",
    "21*x1 + 23*x2 + 16*x3 >= 45",
    "13*x0 + 23*x2 + 16*x3 >= 53",
    "13*x0 + 23*x2 + 9*x4 >= 53",
    "21*x1 + 16*x3 + 9*x4 >= 53",
    "21*x1 + 23*x2 + 16*x3 >= 53",
    "13*x0 + 23*x2 + 16*x3 >= 60",
    "13*x0 + 23*x2 + 9*x4 >= 60",
    "21*x1 + 16*x3 + 9*x4 >= 60",
    "21*x1 + 23*x2 + 16*x3 >= 60",
    "13*x0 + 23*x2 + 16*x3 >= 78",
    "13*x0 + 23*x2 + 9*x4 >= 78",
    "21*x1 + 16*x3 + 9*x4 >= 78",
    "21*x1 + 23*x2 + 16*x3 >= 78",
    "8*x0 - 3*x1 + 10*x2 >= 0",
    "21*x1 + 9*x4 <= 275",
    "16*x3 + 9*x4 <= 144",
    "13*x0 + 21*x1 + 9*x4 <= 197",
    "13*x0 + 23*x2 + 9*x4 <= 265",
    "13*x0 + 21*x1 + 23*x2 + 16*x3 + 9*x4 <= 265",
    "13*x0 + 21*x1 + 23*x2 + 16*x3 + 9*x4 <= 401" 
  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    x = m.addVars(5, lb=0.0, names=["B1", "B5", "B3", "B2", "K"])

    # Set objective function
    m.setObjective(2*x[0] + 3*x[1] + 3*x[2] + 5*x[3] + 9*x[4], gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(13*x[0] + 21*x[1] >= 46)
    m.addConstr(23*x[2] + 16*x[3] >= 37)
    m.addConstr(21*x[1] + 16*x[3] >= 33)
    m.addConstr(13*x[0] + 23*x[2] >= 51)
    m.addConstr(21*x[1] + 23*x[2] >= 59)
    m.addConstr(16*x[3] + 9*x[4] >= 33)
    m.addConstr(13*x[0] + 9*x[4] >= 53)
    m.addConstr(13*x[0] + 23*x[2] + 16*x[3] >= 45)
    m.addConstr(13*x[0] + 23*x[2] + 9*x[4] >= 45)
    m.addConstr(21*x[1] + 16*x[3] + 9*x[4] >= 45)
    m.addConstr(21*x[1] + 23*x[2] + 16*x[3] >= 45)
    m.addConstr(13*x[0] + 23*x[2] + 16*x[3] >= 53)
    m.addConstr(13*x[0] + 23*x[2] + 9*x[4] >= 53)
    m.addConstr(21*x[1] + 16*x[3] + 9*x[4] >= 53)
    m.addConstr(21*x[1] + 23*x[2] + 16*x[3] >= 53)
    m.addConstr(13*x[0] + 23*x[2] + 16*x[3] >= 60)
    m.addConstr(13*x[0] + 23*x[2] + 9*x[4] >= 60)
    m.addConstr(21*x[1] + 16*x[3] + 9*x[4] >= 60)
    m.addConstr(21*x[1] + 23*x[2] + 16*x[3] >= 60)
    m.addConstr(13*x[0] + 23*x[2] + 16*x[3] >= 78)
    m.addConstr(13*x[0] + 23*x[2] + 9*x[4] >= 78)
    m.addConstr(21*x[1] + 16*x[3] + 9*x[4] >= 78)
    m.addConstr(21*x[1] + 23*x[2] + 16*x[3] >= 78)
    m.addConstr(8*x[0] - 3*x[1] + 10*x[2] >= 0)
    m.addConstr(21*x[1] + 9*x[4] <= 275)
    m.addConstr(16*x[3] + 9*x[4] <= 144)
    m.addConstr(13*x[0] + 21*x[1] + 9*x[4] <= 197)
    m.addConstr(13*x[0] + 23*x[2] + 9*x[4] <= 265)
    m.addConstr(13*x[0] + 21*x[1] + 23*x[2] + 16*x[3] + 9*x[4] <= 265)
    m.addConstr(13*x[0] + 21*x[1] + 23*x[2] + 16*x[3] + 9*x[4] <= 401)


    # Optimize model
    m.optimize()

    if m.status == gp.GRB.OPTIMAL:
        for v in m.getVars():
            print(f'{v.varName}: {v.x}')
        print(f'Obj: {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')
```
