Here's the Gurobi code to solve the optimization problem.  We first define the objective function coefficients and constraint data.  The constraints are converted to consistent units (square inches) and the model is built and solved.

```python
import gurobipy as gp

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

    # Create variables
    verbenas = m.addVar(vtype=gp.GRB.INTEGER, name="verbenas")
    agave = m.addVar(vtype=gp.GRB.INTEGER, name="agave")
    peonies = m.addVar(vtype=gp.GRB.INTEGER, name="peonies")
    geraniums = m.addVar(vtype=gp.GRB.INTEGER, name="geraniums")

    # Set objective function
    m.setObjective(3.32 * verbenas + 8.56 * agave + 3.32 * peonies + 8.22 * geraniums, gp.GRB.MAXIMIZE)

    # Add constraints (converting sq ft to sq in where necessary)
    m.addConstr(26 * verbenas + 11 * agave + 20 * peonies + 9 * geraniums <= 193, "Planting Space")
    m.addConstr(20 * verbenas + 5 * agave + 11 * peonies + 11 * geraniums <= 257, "Resilience Index")
    m.addConstr(20 * peonies + 9 * geraniums >= 33 * 144, "Peonies + Geraniums Space") # 33 sq ft
    m.addConstr(26 * verbenas + 9 * geraniums >= 28 * 144, "Verbenas + Geraniums Space") # 28 sq ft
    m.addConstr(20 * verbenas + 11 * geraniums >= 28, "Verbenas + Geraniums Resilience")
    m.addConstr(20 * verbenas + 5 * agave + 11 * peonies >= 42, "Verbenas + Agave + Peonies Resilience")
    m.addConstr(20 * verbenas + 11 * peonies + 11 * geraniums >= 42, "Verbenas + Peonies + Geraniums Resilience")
    m.addConstr(20 * verbenas + 5 * agave + 11 * peonies >= 55, "Verbenas + Agave + Peonies Resilience 2")
    m.addConstr(20 * verbenas + 11 * peonies + 11 * geraniums >= 55, "Verbenas + Peonies + Geraniums Resilience 2")
    m.addConstr(11 * agave + 9 * geraniums <= 168 * 144, "Agave + Geraniums Space") # 168 sq ft
    m.addConstr(26 * verbenas + 11 * agave + 20 * peonies + 9 * geraniums <= 168 * 144, "Total Space") # 168 sq ft
    m.addConstr(5 * agave + 11 * peonies <= 175, "Agave + Peonies Resilience")
    m.addConstr(20 * verbenas + 11 * peonies <= 95, "Verbenas + Peonies Resilience")
    m.addConstr(20 * verbenas + 11 * geraniums <= 117, "Verbenas + Geraniums Resilience Limit")
    m.addConstr(5 * agave + 11 * geraniums <= 210, "Agave + Geraniums Resilience")
    m.addConstr(20 * verbenas + 5 * agave + 11 * peonies + 11 * geraniums <= 210, "Total Resilience")


    # 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')
```