## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are ['verbenas', 'agave', 'peonies', 'geraniums'], which we can represent symbolically as ['x0', 'x1', 'x2', 'x3']. The objective function to maximize is $3.32x_0 + 8.56x_1 + 3.32x_2 + 8.22x_3$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
1. $26x_0 + 11x_1 + 20x_2 + 9x_3 \leq 193$ (planting space constraint)
2. $20x_0 + 5x_1 + 11x_2 + 11x_3 \leq 257$ (resilience index constraint)
3. $20x_2 + 9x_3 \geq 33 \times 144$ (peonies and geraniums space constraint, converting sq. ft to sq. in)
4. $26x_0 + 9x_3 \geq 28 \times 144$ (verbenas and geraniums space constraint, converting sq. ft to sq. in)
5. $20x_0 + 11x_3 \geq 28$ (verbenas and geraniums resilience index constraint)
6. $20x_0 + 5x_1 + 11x_2 \geq 42$ (verbenas, agave, and peonies resilience index constraint)
7. $20x_0 + 11x_2 + 11x_3 \geq 42$ (verbenas, peonies, and geraniums resilience index constraint)
8. $20x_0 + 5x_1 + 11x_2 \geq 55$ (verbenas, agave, and peonies resilience index constraint, alternative)
9. $20x_0 + 11x_2 + 11x_3 \geq 55$ (verbenas, peonies, and geraniums resilience index constraint, alternative)
10. $11x_1 + 9x_3 \leq 168 \times 144$ (agave and geraniums space constraint, converting sq. ft to sq. in)
11. $26x_0 + 11x_1 + 20x_2 + 9x_3 \leq 168 \times 144$ (total planting space constraint for all, converting sq. ft to sq. in)
12. $5x_1 + 11x_2 \leq 175$ (agave and peonies resilience index constraint)
13. $20x_0 + 11x_2 \leq 95$ (verbenas and peonies resilience index constraint)
14. $20x_0 + 11x_3 \leq 117$ (verbenas and geraniums resilience index constraint)
15. $5x_1 + 11x_3 \leq 210$ (agave and geraniums resilience index constraint)
16. $20x_0 + 5x_1 + 11x_2 + 11x_3 \leq 210$ (total resilience index constraint)

## 3: Correct and simplify constraints for consistency and accuracy
Correcting and ensuring all constraints are properly represented:
- The space constraints should be in square inches, so $1$ sq. ft $= 144$ sq. in.
- Constraints 3 and 4 need conversion: $33$ sq. ft $= 33 \times 144$ sq. in, $28$ sq. ft $= 28 \times 144$ sq. in.

## 4: Formulate the symbolic representation
```json
{
    'sym_variables': [('x0', 'verbenas'), ('x1', 'agave'), ('x2', 'peonies'), ('x3', 'geraniums')],
    'objective_function': '3.32*x0 + 8.56*x1 + 3.32*x2 + 8.22*x3',
    'constraints': [
        '26*x0 + 11*x1 + 20*x2 + 9*x3 <= 193',
        '20*x0 + 5*x1 + 11*x2 + 11*x3 <= 257',
        '20*x2 + 9*x3 >= 4752',
        '26*x0 + 9*x3 >= 4032',
        '20*x0 + 11*x3 >= 28',
        '20*x0 + 5*x1 + 11*x2 >= 42',
        '20*x0 + 11*x2 + 11*x3 >= 42',
        '20*x0 + 5*x1 + 11*x2 >= 55',
        '20*x0 + 11*x2 + 11*x3 >= 55',
        '11*x1 + 9*x3 <= 24192',
        '26*x0 + 11*x1 + 20*x2 + 9*x3 <= 24192',
        '5*x1 + 11*x2 <= 175',
        '20*x0 + 11*x2 <= 95',
        '20*x0 + 11*x3 <= 117',
        '5*x1 + 11*x3 <= 210',
        '20*x0 + 5*x1 + 11*x2 + 11*x3 <= 210'
    ]
}
```

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

# Define the model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name='verbenas', vtype=gurobi.GRB.INTEGER)
x1 = model.addVar(name='agave', vtype=gurobi.GRB.INTEGER)
x2 = model.addVar(name='peonies', vtype=gurobi.GRB.INTEGER)
x3 = model.addVar(name='geraniums', vtype=gurobi.GRB.INTEGER)

# Define the objective function
model.setObjective(3.32*x0 + 8.56*x1 + 3.32*x2 + 8.22*x3, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(26*x0 + 11*x1 + 20*x2 + 9*x3 <= 193)
model.addConstr(20*x0 + 5*x1 + 11*x2 + 11*x3 <= 257)
model.addConstr(20*x2 + 9*x3 >= 4752)
model.addConstr(26*x0 + 9*x3 >= 4032)
model.addConstr(20*x0 + 11*x3 >= 28)
model.addConstr(20*x0 + 5*x1 + 11*x2 >= 42)
model.addConstr(20*x0 + 11*x2 + 11*x3 >= 42)
model.addConstr(20*x0 + 5*x1 + 11*x2 >= 55)
model.addConstr(20*x0 + 11*x2 + 11*x3 >= 55)
model.addConstr(11*x1 + 9*x3 <= 24192)
model.addConstr(26*x0 + 11*x1 + 20*x2 + 9*x3 <= 24192)
model.addConstr(5*x1 + 11*x2 <= 175)
model.addConstr(20*x0 + 11*x2 <= 95)
model.addConstr(20*x0 + 11*x3 <= 117)
model.addConstr(5*x1 + 11*x3 <= 210)
model.addConstr(20*x0 + 5*x1 + 11*x2 + 11*x3 <= 210)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objVal)
    print('Verbenas: ', x0.varValue)
    print('Agave: ', x1.varValue)
    print('Peonies: ', x2.varValue)
    print('Geraniums: ', x3.varValue)
else:
    print('The model is infeasible')
```