```json
{
  "sym_variables": [
    ("x0", "aloe vera"),
    ("x1", "cherry trees")
  ],
  "objective_function": "6*x0 + 7*x1",
  "constraints": [
    "1*x0 + 15*x1 >= 39",
    "7*x0 + 16*x1 >= 14",
    "9*x0 + 12*x1 >= 38",
    "14*x0 + 18*x1 >= 26",
    "-4*x0 + 7*x1 >= 0",
    "1*x0 + 15*x1 <= 72",
    "7*x0 + 16*x1 <= 55",
    "9*x0 + 12*x1 <= 99",
    "14*x0 + 18*x1 <= 57"
  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    aloe_vera = m.addVar(vtype=gp.GRB.INTEGER, name="aloe_vera")
    cherry_trees = m.addVar(vtype=gp.GRB.INTEGER, name="cherry_trees")

    # Set objective function
    m.setObjective(6 * aloe_vera + 7 * cherry_trees, gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(1 * aloe_vera + 15 * cherry_trees >= 39, "cost_lower_bound")
    m.addConstr(7 * aloe_vera + 16 * cherry_trees >= 14, "water_lower_bound")
    m.addConstr(9 * aloe_vera + 12 * cherry_trees >= 38, "yield_lower_bound")
    m.addConstr(14 * aloe_vera + 18 * cherry_trees >= 26, "resilience_lower_bound")
    m.addConstr(-4 * aloe_vera + 7 * cherry_trees >= 0, "custom_constraint_1")
    m.addConstr(1 * aloe_vera + 15 * cherry_trees <= 72, "cost_upper_bound")
    m.addConstr(7 * aloe_vera + 16 * cherry_trees <= 55, "water_upper_bound")
    m.addConstr(9 * aloe_vera + 12 * cherry_trees <= 99, "yield_upper_bound")
    m.addConstr(14 * aloe_vera + 18 * cherry_trees <= 57, "resilience_upper_bound")


    # Optimize model
    m.optimize()

    # Print results
    if m.status == gp.GRB.OPTIMAL:
        print('Obj: %g' % m.objVal)
        print('aloe_vera:', aloe_vera.x)
        print('cherry_trees:', cherry_trees.x)
    elif m.status == gp.GRB.INFEASIBLE:
        print('The 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')
```