## Step 1: Define the symbolic representation of the variables
The variables are 'chili plants', 'lilies', 'decorative cabbages', and 'ferns', which can be represented symbolically as $x_0, x_1, x_2, x_3$ respectively.

## Step 2: Convert the objective function into symbolic notation
The objective function to maximize is $9.63x_0 + 1.35x_1 + 9.63x_2 + 7.06x_3$.

## Step 3: List the constraints in symbolic notation
The constraints are:
- $26x_1 + 12x_2 \geq 94$
- $18x_0 + 12x_2 \geq 48$
- $12x_2 + 18x_3 \geq 74$
- $26x_1 + 18x_3 \geq 71$
- $18x_0 + 26x_1 \geq 51$
- $18x_0 + 12x_2 + 18x_3 \geq 74$
- $20x_0 + 23x_3 \geq 65$
- $20x_0 + 8x_2 \geq 43$
- $8x_2 + 23x_3 \geq 58$
- $23x_1 + 8x_2 \geq 87$
- $20x_0 + 23x_1 \geq 77$
- $26x_1 + 12x_2 \leq 317$
- $18x_0 + 18x_3 \leq 380$
- $18x_0 + 12x_2 \leq 243$
- $18x_0 + 26x_1 + 12x_2 + 18x_3 \leq 243$
- $23x_1 + 8x_2 \leq 227$
- $23x_1 + 23x_3 \leq 107$
- $20x_0 + 8x_2 + 23x_3 \leq 304$
- $20x_0 + 23x_1 + 8x_2 + 23x_3 \leq 304$

## 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'chili plants'),
        ('x1', 'lilies'),
        ('x2', 'decorative cabbages'),
        ('x3', 'ferns')
    ],
    'objective_function': '9.63*x0 + 1.35*x1 + 9.63*x2 + 7.06*x3',
    'constraints': [
        '26*x1 + 12*x2 >= 94',
        '18*x0 + 12*x2 >= 48',
        '12*x2 + 18*x3 >= 74',
        '26*x1 + 18*x3 >= 71',
        '18*x0 + 26*x1 >= 51',
        '18*x0 + 12*x2 + 18*x3 >= 74',
        '20*x0 + 23*x3 >= 65',
        '20*x0 + 8*x2 >= 43',
        '8*x2 + 23*x3 >= 58',
        '23*x1 + 8*x2 >= 87',
        '20*x0 + 23*x1 >= 77',
        '26*x1 + 12*x2 <= 317',
        '18*x0 + 18*x3 <= 380',
        '18*x0 + 12*x2 <= 243',
        '18*x0 + 26*x1 + 12*x2 + 18*x3 <= 243',
        '23*x1 + 8*x2 <= 227',
        '23*x1 + 23*x3 <= 107',
        '20*x0 + 8*x2 + 23*x3 <= 304',
        '20*x0 + 23*x1 + 8*x2 + 23*x3 <= 304'
    ]
}
```

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

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

    # Define the variables
    x0 = model.addVar(name="chili_plants", vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name="lilies", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="decorative_cabbages", vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name="ferns", vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(9.63 * x0 + 1.35 * x1 + 9.63 * x2 + 7.06 * x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(26 * x1 + 12 * x2 >= 94)
    model.addConstr(18 * x0 + 12 * x2 >= 48)
    model.addConstr(12 * x2 + 18 * x3 >= 74)
    model.addConstr(26 * x1 + 18 * x3 >= 71)
    model.addConstr(18 * x0 + 26 * x1 >= 51)
    model.addConstr(18 * x0 + 12 * x2 + 18 * x3 >= 74)
    model.addConstr(20 * x0 + 23 * x3 >= 65)
    model.addConstr(20 * x0 + 8 * x2 >= 43)
    model.addConstr(8 * x2 + 23 * x3 >= 58)
    model.addConstr(23 * x1 + 8 * x2 >= 87)
    model.addConstr(20 * x0 + 23 * x1 >= 77)
    model.addConstr(26 * x1 + 12 * x2 <= 317)
    model.addConstr(18 * x0 + 18 * x3 <= 380)
    model.addConstr(18 * x0 + 12 * x2 <= 243)
    model.addConstr(18 * x0 + 26 * x1 + 12 * x2 + 18 * x3 <= 243)
    model.addConstr(23 * x1 + 8 * x2 <= 227)
    model.addConstr(23 * x1 + 23 * x3 <= 107)
    model.addConstr(20 * x0 + 8 * x2 + 23 * x3 <= 304)
    model.addConstr(20 * x0 + 23 * x1 + 8 * x2 + 23 * x3 <= 304)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("Chili plants: ", x0.varValue)
        print("Lilies: ", x1.varValue)
        print("Decorative cabbages: ", x2.varValue)
        print("Ferns: ", x3.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```