```json
{
  "sym_variables": [
    ("x0", "agave"),
    ("x1", "chives"),
    ("x2", "boxwoods")
  ],
  "objective_function": "5*x0 + 3*x1 + 5*x2",
  "constraints": [
    "3*x0 + 5*x1 >= 3",
    "3*x0 + 5*x1 + 2*x2 >= 4",
    "5*x0 + 1*x1 >= 24",
    "1*x1 + 3*x2 >= 20",
    "5*x0 + 3*x2 >= 15",
    "5*x0 + 1*x1 + 3*x2 >= 19",
    "2*x1 + 4*x2 >= 13",
    "3*x0 + 5*x1 <= 27",
    "5*x1 + 2*x2 <= 10",
    "3*x0 + 5*x1 + 2*x2 <= 20",
    "5*x0 + 1*x1 <= 70",
    "1*x1 + 3*x2 <= 95",
    "5*x0 + 3*x2 <= 47",
    "5*x0 + 1*x1 + 3*x2 <= 47",
    "4*x0 + 4*x2 <= 36",
    "2*x1 + 4*x2 <= 27",
    "4*x0 + 2*x1 + 4*x2 <= 27"
  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    agave = m.addVar(vtype=gp.GRB.INTEGER, name="agave")
    chives = m.addVar(vtype=gp.GRB.INTEGER, name="chives")
    boxwoods = m.addVar(vtype=gp.GRB.INTEGER, name="boxwoods")

    # Set objective function
    m.setObjective(5 * agave + 3 * chives + 5 * boxwoods, gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(3 * agave + 5 * chives >= 3, "c1")
    m.addConstr(3 * agave + 5 * chives + 2 * boxwoods >= 4, "c2")
    m.addConstr(5 * agave + 1 * chives >= 24, "c3")
    m.addConstr(1 * chives + 3 * boxwoods >= 20, "c4")
    m.addConstr(5 * agave + 3 * boxwoods >= 15, "c5")
    m.addConstr(5 * agave + 1 * chives + 3 * boxwoods >= 19, "c6")
    m.addConstr(2 * chives + 4 * boxwoods >= 13, "c7")
    m.addConstr(3 * agave + 5 * chives <= 27, "c8")
    m.addConstr(5 * chives + 2 * boxwoods <= 10, "c9")
    m.addConstr(3 * agave + 5 * chives + 2 * boxwoods <= 20, "c10")
    m.addConstr(5 * agave + 1 * chives <= 70, "c11")
    m.addConstr(1 * chives + 3 * boxwoods <= 95, "c12")
    m.addConstr(5 * agave + 3 * boxwoods <= 47, "c13")
    m.addConstr(5 * agave + 1 * chives + 3 * boxwoods <= 47, "c14")
    m.addConstr(4 * agave + 4 * boxwoods <= 36, "c15")
    m.addConstr(2 * chives + 4 * boxwoods <= 27, "c16")
    m.addConstr(4 * agave + 2 * chives + 4 * boxwoods <= 27, "c17")


    # 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('Optimization problem 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')
```
