```json
{
  "sym_variables": [
    ("x0", "boxwoods"),
    ("x1", "roses"),
    ("x2", "begonias")
  ],
  "objective_function": "8.66 * x0 + 8.39 * x1 + 3.72 * x2",
  "constraints": [
    "8.65 * x0 + 1.12 * x1 + 0.8 * x2 >= 28 * 144",
    "9.06 * x0 + 5.81 * x2 >= 32",
    "6.61 * x1 + 5.81 * x2 >= 18",
    "9.06 * x0 + 6.61 * x1 + 5.81 * x2 >= 20",
    "8.65 * x0 + 0.8 * x2 <= 135 * 144",
    "1.12 * x1 + 0.8 * x2 <= 68 * 144",
    "8.65 * x0 + 1.12 * x1 <= 128 * 144",
    "8.65 * x0 + 1.12 * x1 + 0.8 * x2 <= 67 * 144",
    "6.61 * x1 + 5.81 * x2 <= 94",
    "9.06 * x0 + 6.61 * x1 <= 35",
    "9.06 * x0 + 6.61 * x1 + 5.81 * x2 <= 35"
  ]
}
```

The constraints related to planting space are converted from sq. ft to sq. inches by multiplying by 144.  The variables x0, x1, and x2 represent the quantities of boxwoods, roses, and begonias, respectively.

```python
import gurobipy as gp

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

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

    # Set objective function
    m.setObjective(8.66 * boxwoods + 8.39 * roses + 3.72 * begonias, gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(8.65 * boxwoods + 1.12 * roses + 0.8 * begonias >= 28 * 144, "PlantingSpaceMin")
    m.addConstr(9.06 * boxwoods + 5.81 * begonias >= 32, "ResilienceBoxwoodsBegoniasMin")
    m.addConstr(6.61 * roses + 5.81 * begonias >= 18, "ResilienceRosesBegoniasMin")
    m.addConstr(9.06 * boxwoods + 6.61 * roses + 5.81 * begonias >= 20, "ResilienceTotalMin")
    m.addConstr(8.65 * boxwoods + 0.8 * begonias <= 135 * 144, "PlantingSpaceBoxwoodsBegoniasMax")
    m.addConstr(1.12 * roses + 0.8 * begonias <= 68 * 144, "PlantingSpaceRosesBegoniasMax")
    m.addConstr(8.65 * boxwoods + 1.12 * roses <= 128 * 144, "PlantingSpaceBoxwoodsRosesMax")
    m.addConstr(8.65 * boxwoods + 1.12 * roses + 0.8 * begonias <= 67 * 144, "PlantingSpaceTotalMax")
    m.addConstr(6.61 * roses + 5.81 * begonias <= 94, "ResilienceRosesBegoniasMax")
    m.addConstr(9.06 * boxwoods + 6.61 * roses <= 35, "ResilienceBoxwoodsRosesMax")
    m.addConstr(9.06 * boxwoods + 6.61 * roses + 5.81 * begonias <= 35, "ResilienceTotalMax")


    # 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("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')
```
