```json
{
  "sym_variables": [
    ("x0", "cucumber vines"),
    ("x1", "carrots"),
    ("x2", "vincas"),
    ("x3", "roses")
  ],
  "objective_function": "6*x0 + 8*x1 + 2*x2 + 1*x3",
  "constraints": [
    "x0 + x2 + x3 >= 24",
    "x0 + x1 + x2 >= 24",
    "x0 + x2 + x3 >= 18",
    "x0 + x1 + x2 >= 18",
    "x2 + x3 >= 34",
    "8*x0 + 1*x2 >= 35",
    "13*x1 + 3*x3 >= 30",
    "8*x0 + 3*x3 >= 22",
    "8*x0 + 13*x1 + 1*x2 >= 34",
    "x0 + x2 <= 81",
    "x1 + x2 <= 107",
    "x1 + x3 <= 99",
    "x0 + x3 <= 141",
    "x2 + x3 <= 68",
    "x0 + x1 <= 60",
    "x0 + x1 + x3 <= 91",
    "x0 + x1 + x2 + x3 <= 91",
    "x2 + x3 <= 53",
    "8*x0 + 3*x3 <= 93",
    "8*x0 + 1*x2 <= 102",
    "8*x0 + 13*x1 + 1*x2 + 3*x3 <= 102",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0",
    "x3 >= 0"
  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    x = m.addVars(4, vtype=gp.GRB.INTEGER, name=["cucumber_vines", "carrots", "vincas", "roses"])

    # Set objective function
    m.setObjective(6*x[0] + 8*x[1] + 2*x[2] + 1*x[3], gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(x[0] + x[2] + x[3] >= 24)
    m.addConstr(x[0] + x[1] + x[2] >= 24)
    m.addConstr(x[0] + x[2] + x[3] >= 18)
    m.addConstr(x[0] + x[1] + x[2] >= 18)
    m.addConstr(x[2] + x[3] >= 34)
    m.addConstr(8*x[0] + x[2] >= 35)
    m.addConstr(13*x[1] + 3*x[3] >= 30)
    m.addConstr(8*x[0] + 3*x[3] >= 22)
    m.addConstr(8*x[0] + 13*x[1] + x[2] >= 34)
    m.addConstr(x[0] + x[2] <= 81)
    m.addConstr(x[1] + x[2] <= 107)
    m.addConstr(x[1] + x[3] <= 99)
    m.addConstr(x[0] + x[3] <= 141)
    m.addConstr(x[2] + x[3] <= 68)
    m.addConstr(x[0] + x[1] <= 60)
    m.addConstr(x[0] + x[1] + x[3] <= 91)
    m.addConstr(x[0] + x[1] + x[2] + x[3] <= 91)
    m.addConstr(x[2] + x[3] <= 53)
    m.addConstr(8*x[0] + 3*x[3] <= 93)
    m.addConstr(8*x[0] + x[2] <= 102)
    m.addConstr(8*x[0] + 13*x[1] + x[2] + 3*x[3] <= 102)


    # Optimize model
    m.optimize()

    if m.status == gp.GRB.OPTIMAL:
        for v in m.getVars():
            print('%s %g' % (v.varName, v.x))
        print('Obj: %g' % m.objVal)
    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')
```