## Step 1: Define the symbolic representation of the variables
The variables are 'boxwoods', 'roses', and 'begonias', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_0$ : boxwoods
- $x_1$ : roses
- $x_2$ : begonias

## 3: Define the objective function in symbolic notation
The objective function to maximize is $8.66x_0 + 8.39x_1 + 3.72x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $8.65x_0 + 1.12x_1 + 0.8x_2 \geq 28 \times 144$ (since 1 square foot = 144 square inches)
- $9.06x_0 + 5.81x_2 \geq 32$
- $6.61x_1 + 5.81x_2 \geq 18$
- $9.06x_0 + 6.61x_1 + 5.81x_2 \geq 20$
- $8.65x_0 + 0.8x_2 \leq 135 \times 144$
- $1.12x_1 + 0.8x_2 \leq 68 \times 144$
- $8.65x_0 + 1.12x_1 \leq 128 \times 144$
- $8.65x_0 + 1.12x_1 + 0.8x_2 \leq 67 \times 144$
- $6.61x_1 + 5.81x_2 \leq 94$
- $9.06x_0 + 6.61x_1 \leq 35$
- $9.06x_0 + 6.61x_1 + 5.81x_2 \leq 35$
- $x_0, x_1, x_2$ are integers.

## 5: Adjust units for consistency
To ensure consistency, convert all measurements to the same unit. Given that most measurements are in square inches, we'll keep it that way for simplicity.

## 6: Create the Gurobi model
We will now create a Gurobi model that represents this optimization problem.

```python
import gurobi

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

# Define the variables
x0 = m.addVar(name="boxwoods", vtype=gurobi.GRB.INTEGER)
x1 = m.addVar(name="roses", vtype=gurobi.GRB.INTEGER)
x2 = m.addVar(name="begonias", vtype=gurobi.GRB.INTEGER)

# Objective function
m.setObjective(8.66 * x0 + 8.39 * x1 + 3.72 * x2, gurobi.GRB.MAXIMIZE)

# Constraints
m.addConstr(8.65 * x0 + 1.12 * x1 + 0.8 * x2 >= 28 * 144)  # Planting space minimum
m.addConstr(9.06 * x0 + 5.81 * x2 >= 32)  # Resilience index minimum for boxwoods and begonias
m.addConstr(6.61 * x1 + 5.81 * x2 >= 18)  # Resilience index minimum for roses and begonias
m.addConstr(9.06 * x0 + 6.61 * x1 + 5.81 * x2 >= 20)  # Total resilience index minimum
m.addConstr(8.65 * x0 + 0.8 * x2 <= 135 * 144)  # Boxwoods and begonias maximum planting space
m.addConstr(1.12 * x1 + 0.8 * x2 <= 68 * 144)  # Roses and begonias maximum planting space
m.addConstr(8.65 * x0 + 1.12 * x1 <= 128 * 144)  # Boxwoods and roses maximum planting space
m.addConstr(8.65 * x0 + 1.12 * x1 + 0.8 * x2 <= 67 * 144)  # Total maximum planting space
m.addConstr(6.61 * x1 + 5.81 * x2 <= 94)  # Resilience index maximum for roses and begonias
m.addConstr(9.06 * x0 + 6.61 * x1 <= 35)  # Resilience index maximum for boxwoods and roses
m.addConstr(9.06 * x0 + 6.61 * x1 + 5.81 * x2 <= 35)  # Total resilience index maximum

# Solve the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Boxwoods: ", x0.varValue)
    print("Roses: ", x1.varValue)
    print("Begonias: ", x2.varValue)
else:
    print("The model is infeasible")
```

## 7: Symbolic Representation
The symbolic representation of the problem is:
```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 >= 4032',
        '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 <= 19440',
        '1.12*x1 + 0.8*x2 <= 9792',
        '8.65*x0 + 1.12*x1 <= 18432',
        '8.65*x0 + 1.12*x1 + 0.8*x2 <= 9648',
        '6.61*x1 + 5.81*x2 <= 94',
        '9.06*x0 + 6.61*x1 <= 35',
        '9.06*x0 + 6.61*x1 + 5.81*x2 <= 35'
    ]
}
```