## Step 1: Define the symbolic representation of the variables
The variables are 'chili plants', 'pansies', and 'roses', 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 objective function to maximize is $8x_0 + 7x_1 + 3x_2$.

## Step 3: List the constraints in symbolic notation
The constraints given are:
- $5x_0 + 2x_2 \leq 13$
- $4x_1 + 2x_2 \leq 11$
- $5x_0 + 4x_1 + 2x_2 \leq 26$
- $5x_0 + x_1 + 2x_2 \leq 26$ (This seems to be a repeat of the previous constraint, so it will be ignored to avoid redundancy)
- $5x_0 + x_1 \leq 21$
- $5x_0 + 2x_2 \leq 11$
- $x_1 + 2x_2 \leq 23$
- $5x_0 + x_1 + 2x_2 \leq 28$
- $x_0, x_1, x_2 \geq 0$ and are integers.

## 4: Correct and simplify the constraints
Upon review, the actual constraints from the problem statement should be accurately represented. Notably, there are repeated or similar constraints that can be streamlined for efficiency. The key constraints are:
- Growth speed: $5x_0 + 2x_2 \leq 13$
- $4x_1 + 2x_2 \leq 11$
- Total growth speed: $5x_0 + 4x_1 + 2x_2 \leq 26$
- Cost constraints:
- $5x_0 + x_1 \leq 21$
- $5x_0 + 2x_2 \leq 11$
- $x_1 + 2x_2 \leq 23$
- $5x_0 + x_1 + 2x_2 \leq 28$

## 5: Create the symbolic representation dictionary
```json
{
'sym_variables': [('x0', 'chili plants'), ('x1', 'pansies'), ('x2', 'roses')],
'objective_function': '8*x0 + 7*x1 + 3*x2',
'constraints': [
    '5*x0 + 2*x2 <= 13',
    '4*x1 + 2*x2 <= 11',
    '5*x0 + 4*x1 + 2*x2 <= 26',
    '5*x0 + x1 <= 21',
    '5*x0 + 2*x2 <= 11',
    'x1 + 2*x2 <= 23',
    '5*x0 + x1 + 2*x2 <= 28',
    'x0 >= 0', 'x1 >= 0', 'x2 >= 0'
]
}
```

## 6: Write the Gurobi code
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x0 = model.addVar(name="chili_plants", vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name="pansies", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="roses", vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(8*x0 + 7*x1 + 3*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(5*x0 + 2*x2 <= 13)
    model.addConstr(4*x1 + 2*x2 <= 11)
    model.addConstr(5*x0 + 4*x1 + 2*x2 <= 26)
    model.addConstr(5*x0 + x1 <= 21)
    model.addConstr(5*x0 + 2*x2 <= 11)
    model.addConstr(x1 + 2*x2 <= 23)
    model.addConstr(5*x0 + x1 + 2*x2 <= 28)

    # Non-negativity constraints (implicit in Gurobi for integer variables)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Chili plants: {x0.varValue}")
        print(f"Pansies: {x1.varValue}")
        print(f"Roses: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```