## Step 1: Define the symbolic representation of the variables
The variables are 'petunias', 'peonies', and 'verbenas', 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:
- $x_0$ : petunias
- $x_1$ : peonies
- $x_2$ : verbenas

## 3: Define the objective function in symbolic notation
The objective function to maximize is $8x_0 + 7x_1 + 7x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $2x_1 + 2x_2 \geq 8$ (resilience index from peonies and verbenas)
- $7x_0 + 2x_1 + 2x_2 \geq 25$ (resilience index from petunias, peonies, and verbenas)
- $5x_0 + 3x_1 \geq 16$ (growth speed from petunias and peonies)
- $5x_0 + 3x_1 + 4x_2 \geq 21$ (growth speed from petunias, peonies, and verbenas)
- $2x_1 + 2x_2 \leq 74$ (resilience index from peonies and verbenas)
- $7x_0 + 2x_1 \leq 69$ (resilience index from petunias and peonies)
- $7x_0 + 2x_2 \leq 47$ (resilience index from petunias and verbenas)
- $7x_0 + 2x_1 + 2x_2 \leq 47$ (resilience index from petunias, peonies, and verbenas)
- $x_0 + 3x_2 \leq 45$ (yield from petunias and verbenas)
- $x_0 + 8x_1 \leq 53$ (yield from petunias and peonies)
- $x_0 + 8x_1 + 3x_2 \leq 53$ (yield from petunias, peonies, and verbenas)
- $3x_1 + 4x_2 \leq 58$ (growth speed from peonies and verbenas)
- $5x_0 + 3x_1 + 4x_2 \leq 58$ (growth speed from petunias, peonies, and verbenas)
- $x_0, x_1, x_2 \geq 0$ and are integers.

## 5: Create the Gurobi model and variables
We will now create a Gurobi model and define the variables.

## 6: Implement the objective function and constraints in Gurobi
```python
import gurobi

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

# Define the variables
x0 = m.addVar(name="petunias", vtype=gurobi.GRB.INTEGER)
x1 = m.addVar(name="peonies", vtype=gurobi.GRB.INTEGER)
x2 = m.addVar(name="verbenas", vtype=gurobi.GRB.INTEGER)

# Objective function
m.setObjective(8*x0 + 7*x1 + 7*x2, gurobi.GRB.MAXIMIZE)

# Constraints
m.addConstr(2*x1 + 2*x2 >= 8)
m.addConstr(7*x0 + 2*x1 + 2*x2 >= 25)
m.addConstr(5*x0 + 3*x1 >= 16)
m.addConstr(5*x0 + 3*x1 + 4*x2 >= 21)
m.addConstr(2*x1 + 2*x2 <= 74)
m.addConstr(7*x0 + 2*x1 <= 69)
m.addConstr(7*x0 + 2*x2 <= 47)
m.addConstr(7*x0 + 2*x1 + 2*x2 <= 47)
m.addConstr(x0 + 3*x2 <= 45)
m.addConstr(x0 + 8*x1 <= 53)
m.addConstr(x0 + 8*x1 + 3*x2 <= 53)
m.addConstr(3*x1 + 4*x2 <= 58)
m.addConstr(5*x0 + 3*x1 + 4*x2 <= 58)

# Optimize the model
m.optimize()

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

## 7: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'petunias'), ('x1', 'peonies'), ('x2', 'verbenas')],
    'objective_function': '8*x0 + 7*x1 + 7*x2',
    'constraints': [
        '2*x1 + 2*x2 >= 8',
        '7*x0 + 2*x1 + 2*x2 >= 25',
        '5*x0 + 3*x1 >= 16',
        '5*x0 + 3*x1 + 4*x2 >= 21',
        '2*x1 + 2*x2 <= 74',
        '7*x0 + 2*x1 <= 69',
        '7*x0 + 2*x2 <= 47',
        '7*x0 + 2*x1 + 2*x2 <= 47',
        'x0 + 3*x2 <= 45',
        'x0 + 8*x1 <= 53',
        'x0 + 8*x1 + 3*x2 <= 53',
        '3*x1 + 4*x2 <= 58',
        '5*x0 + 3*x1 + 4*x2 <= 58'
    ]
}
```