## Step 1: Define the symbolic representation of the variables
The variables are 'ferns', 'petunias', and 'strawberry bushes', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into symbolic notation
The objective function to maximize is $4x_1 + 5x_2 + 4x_3$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
1. $12x_2 + 4x_3 \geq 49$
2. $-8x_1 + 4x_3 \geq 0$
3. $12x_2 + 4x_3 \leq 180$
4. $9x_1 + 4x_3 \leq 89$
5. $9x_1 + 12x_2 + 4x_3 \leq 89$
6. $x_1, x_2, x_3$ are integers.

## 4: Define the beauty ratings and other given information
The beauty ratings are given as:
- $x_0 = 9$ for $x_1$ (ferns)
- $x_1 = 12$ for $x_2$ (petunias)
- $x_2 = 4$ for $x_3$ (strawberry bushes)

However, these seem to be coefficients or attributes rather than variables. The actual variables and their coefficients in the constraints and objective function are already considered.

## 5: Create a symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'ferns'), ('x2', 'petunias'), ('x3', 'strawberry bushes')],
'objective_function': '4*x1 + 5*x2 + 4*x3',
'constraints': [
'12*x2 + 4*x3 >= 49',
'-8*x1 + 4*x3 >= 0',
'12*x2 + 4*x3 <= 180',
'9*x1 + 4*x3 <= 89',
'9*x1 + 12*x2 + 4*x3 <= 89',
'x1 >= 0', 'x2 >= 0', 'x3 >= 0',
'x1 % 1 == 0', 'x2 % 1 == 0', 'x3 % 1 == 0'
]
}
```

## 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="ferns", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="petunias", vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name="strawberry_bushes", vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(4*x1 + 5*x2 + 4*x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(12*x2 + 4*x3 >= 49)
    model.addConstr(-8*x1 + 4*x3 >= 0)
    model.addConstr(12*x2 + 4*x3 <= 180)
    model.addConstr(9*x1 + 4*x3 <= 89)
    model.addConstr(9*x1 + 12*x2 + 4*x3 <= 89)

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

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Ferns: ", x1.varValue)
        print("Petunias: ", x2.varValue)
        print("Strawberry bushes: ", x3.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```