## Step 1: Define the optimization problem and identify the variables, objective function, and constraints.
The optimization problem is to maximize the objective function: $3.32 \times \text{verbenas} + 8.56 \times \text{agave} + 3.32 \times \text{peonies} + 8.22 \times \text{geraniums}$.

## 2: List the variables and their corresponding coefficients in the objective function.
- $\text{verbenas}$: coefficient = 3.32
- $\text{agave}$: coefficient = 8.56
- $\text{peonies}$: coefficient = 3.32
- $\text{geraniums}$: coefficient = 8.22

## 3: Define the constraints based on the given problem description.
### Resource constraints
- Planting space for verbenas: $26 \times \text{verbenas}$
- Planting space for agave: $11 \times \text{agave}$
- Planting space for peonies: $20 \times \text{peonies}$
- Planting space for geraniums: $9 \times \text{geraniums}$
- Total planting space upper bound: $193$

### Resilience index constraints
- Resilience index for verbenas: $20 \times \text{verbenas}$
- Resilience index for agave: $5 \times \text{agave}$
- Resilience index for peonies: $11 \times \text{peonies}$
- Resilience index for geraniums: $11 \times \text{geraniums}$

## 4: Translate the constraints into mathematical expressions.
1. $26\text{verbenas} + 11\text{agave} + 20\text{peonies} + 9\text{geraniums} \leq 193$
2. $20\text{verbenas} + 5\text{agave} + 11\text{peonies} + 11\text{geraniums} \leq 257$
3. $\text{peonies} + \text{geraniums} \geq 33 \times 144 / 144$ (convert sq. ft to sq. in, $1 \text{ ft}^2 = 144 \text{ in}^2$)
4. $26\text{verbenas} + 9\text{geraniums} \geq 28 \times 144 / 144$
5. $20\text{verbenas} + 11\text{geraniums} \geq 28$
6. $20\text{verbenas} + 11\text{peonies} + 11\text{geraniums} \geq 42$
7. $20\text{verbenas} + 11\text{peonies} + 11\text{geraniums} \geq 42$ (same as above)
8. $20\text{verbenas} + 5\text{agave} + 11\text{peonies} \geq 55$
9. $20\text{verbenas} + 11\text{peonies} + 11\text{geraniums} \geq 55$ (same as constraint 6)
10. $11\text{agave} + 9\text{geraniums} \leq 168 \times 144 / 144$
11. $26\text{verbenas} + 11\text{agave} + 20\text{peonies} + 9\text{geraniums} \leq 168 \times 144 / 144$
12. $5\text{agave} + 11\text{peonies} \leq 175$
13. $20\text{verbenas} + 11\text{peonies} \leq 95$
14. $20\text{verbenas} + 11\text{geraniums} \leq 117$
15. $5\text{agave} + 11\text{geraniums} \leq 210$
16. $20\text{verbenas} + 5\text{agave} + 11\text{peonies} + 11\text{geraniums} \leq 210$

## 5: Implement the optimization problem using Gurobi.
```python
import gurobi

# Create a new Gurobi model
m = gurobi.Model()

# Define the variables
verbenas = m.addVar(vtype=gurobi.GRB.INTEGER, name="verbenas")
agave = m.addVar(vtype=gurobi.GRB.INTEGER, name="agave")
peonies = m.addVar(vtype=gurobi.GRB.INTEGER, name="peonies")
geraniums = m.addVar(vtype=gurobi.GRB.INTEGER, name="geraniums")

# Objective function
m.setObjective(3.32 * verbenas + 8.56 * agave + 3.32 * peonies + 8.22 * geraniums, gurobi.GRB.MAXIMIZE)

# Constraints
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(peonies + geraniums >= 33, "peonies_geraniums_space")
m.addConstr(26 * verbenas + 9 * geraniums >= 28 * 144 / 144, "verbenas_geraniums_space")
m.addConstr(20 * verbenas + 11 * geraniums >= 28, "verbenas_geraniums_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")
m.addConstr(11 * agave + 9 * geraniums <= 168, "agave_geraniums_space")
m.addConstr(26 * verbenas + 11 * agave + 20 * peonies + 9 * geraniums <= 168 * 144 / 144, "total_space")
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_max")
m.addConstr(5 * agave + 11 * geraniums <= 210, "agave_geraniums_resilience_max")
m.addConstr(20 * verbenas + 5 * agave + 11 * peonies + 11 * geraniums <= 210, "total_resilience")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Verbenas: {verbenas.varValue}")
    print(f"Agave: {agave.varValue}")
    print(f"Peonies: {peonies.varValue}")
    print(f"Geraniums: {geraniums.varValue}")
    print(f"Objective: {m.objVal}")
else:
    print("No optimal solution found.")
```