## Step 1: Define the symbolic representation of the variables
The variables are 'orange trees', 'basil plants', 'peonies', 'vincas', which we will represent symbolically as $x_1, x_2, x_3, x_4$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $9.23x_1 + 5.85x_2 + 9.02x_3 + 8.78x_4$.

## Step 3: List all the constraints in symbolic notation
1. $12.3x_1 + 11.72x_3 + 8.97x_4 \geq 35$ (growth speed from peonies and vincas)
2. $8.27x_2 + 8.97x_4 \geq 39$ (growth speed from basil plants and vincas)
3. $12.3x_1 + 8.97x_4 \geq 83$ (growth speed from orange trees and vincas)
4. $12.3x_1 + 8.27x_2 + 11.72x_3 + 8.97x_4 \geq 83$ (total growth speed)
5. $13.88x_1 + 3.73x_2 \geq 37$ (spend on orange trees and basil plants)
6. $7.2x_3 + 4.61x_4 \geq 16$ (spend on peonies and vincas)
7. $13.88x_1 + 4.61x_4 \geq 44$ (spend on orange trees and vincas)
8. $13.88x_1 + 3.73x_2 + 4.61x_4 \geq 33$ (spend on orange trees, basil plants, and vincas)
9. $13.88x_1 + 3.73x_2 + 7.2x_3 + 4.61x_4 \geq 33$ (total spend)
10. $21.16x_1 + 17.79x_4 \geq 91$ (beauty rating from orange trees and vincas)
11. $0.82x_3 + 17.79x_4 \geq 57$ (beauty rating from peonies and vincas)
12. $21.16x_1 + 12.95x_2 \geq 91$ (beauty rating from orange trees and basil plants)
13. $21.16x_1 + 0.82x_3 \geq 106$ (beauty rating from orange trees and peonies)
14. $21.16x_1 + 12.95x_2 + 0.82x_3 + 17.79x_4 \geq 106$ (total beauty rating)
15. $-3x_2 + 8x_4 \geq 0$
16. $-7x_2 + 9x_3 \geq 0$
17. $8.27x_2 + 11.72x_3 \leq 170$ (growth speed from basil plants and peonies)
18. $8.27x_2 + 8.97x_4 \leq 112$ (growth speed from basil plants and vincas)
19. $12.3x_1 + 8.27x_2 + 11.72x_3 \leq 264$ (growth speed from orange trees, basil plants, and peonies)
20. $3.73x_2 + 7.2x_3 \leq 65$ (spend on basil plants and peonies)
21. $21.16x_1 + 0.82x_3 + 17.79x_4 \leq 445$ (total beauty rating from orange trees, peonies, and vincas)

## Step 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'orange trees'), ('x2', 'basil plants'), ('x3', 'peonies'), ('x4', 'vincas')],
    'objective_function': '9.23*x1 + 5.85*x2 + 9.02*x3 + 8.78*x4',
    'constraints': [
        '12.3*x1 + 11.72*x3 + 8.97*x4 >= 35',
        '8.27*x2 + 8.97*x4 >= 39',
        '12.3*x1 + 8.97*x4 >= 83',
        '12.3*x1 + 8.27*x2 + 11.72*x3 + 8.97*x4 >= 83',
        '13.88*x1 + 3.73*x2 >= 37',
        '7.2*x3 + 4.61*x4 >= 16',
        '13.88*x1 + 4.61*x4 >= 44',
        '13.88*x1 + 3.73*x2 + 4.61*x4 >= 33',
        '13.88*x1 + 3.73*x2 + 7.2*x3 + 4.61*x4 >= 33',
        '21.16*x1 + 17.79*x4 >= 91',
        '0.82*x3 + 17.79*x4 >= 57',
        '21.16*x1 + 12.95*x2 >= 91',
        '21.16*x1 + 0.82*x3 >= 106',
        '21.16*x1 + 12.95*x2 + 0.82*x3 + 17.79*x4 >= 106',
        '-3*x2 + 8*x4 >= 0',
        '-7*x2 + 9*x3 >= 0',
        '8.27*x2 + 11.72*x3 <= 170',
        '8.27*x2 + 8.97*x4 <= 112',
        '12.3*x1 + 8.27*x2 + 11.72*x3 <= 264',
        '3.73*x2 + 7.2*x3 <= 65',
        '21.16*x1 + 0.82*x3 + 17.79*x4 <= 445'
    ]
}
```

## Step 5: Write the Gurobi code for the optimization problem
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="x1", vtype=gurobi.GRB.INTEGER)  # orange trees
    x2 = model.addVar(name="x2", vtype=gurobi.GRB.INTEGER)  # basil plants
    x3 = model.addVar(name="x3", vtype=gurobi.GRB.INTEGER)  # peonies
    x4 = model.addVar(name="x4", vtype=gurobi.GRB.INTEGER)  # vincas

    # Objective function
    model.setObjective(9.23 * x1 + 5.85 * x2 + 9.02 * x3 + 8.78 * x4, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(12.3 * x1 + 11.72 * x3 + 8.97 * x4 >= 35)
    model.addConstr(8.27 * x2 + 8.97 * x4 >= 39)
    model.addConstr(12.3 * x1 + 8.97 * x4 >= 83)
    model.addConstr(12.3 * x1 + 8.27 * x2 + 11.72 * x3 + 8.97 * x4 >= 83)
    model.addConstr(13.88 * x1 + 3.73 * x2 >= 37)
    model.addConstr(7.2 * x3 + 4.61 * x4 >= 16)
    model.addConstr(13.88 * x1 + 4.61 * x4 >= 44)
    model.addConstr(13.88 * x1 + 3.73 * x2 + 4.61 * x4 >= 33)
    model.addConstr(13.88 * x1 + 3.73 * x2 + 7.2 * x3 + 4.61 * x4 >= 33)
    model.addConstr(21.16 * x1 + 17.79 * x4 >= 91)
    model.addConstr(0.82 * x3 + 17.79 * x4 >= 57)
    model.addConstr(21.16 * x1 + 12.95 * x2 >= 91)
    model.addConstr(21.16 * x1 + 0.82 * x3 >= 106)
    model.addConstr(21.16 * x1 + 12.95 * x2 + 0.82 * x3 + 17.79 * x4 >= 106)
    model.addConstr(-3 * x2 + 8 * x4 >= 0)
    model.addConstr(-7 * x2 + 9 * x3 >= 0)
    model.addConstr(8.27 * x2 + 11.72 * x3 <= 170)
    model.addConstr(8.27 * x2 + 8.97 * x4 <= 112)
    model.addConstr(12.3 * x1 + 8.27 * x2 + 11.72 * x3 <= 264)
    model.addConstr(3.73 * x2 + 7.2 * x3 <= 65)
    model.addConstr(21.16 * x1 + 0.82 * x3 + 17.79 * x4 <= 445)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("x1 (orange trees): ", x1.varValue)
        print("x2 (basil plants): ", x2.varValue)
        print("x3 (peonies): ", x3.varValue)
        print("x4 (vincas): ", x4.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```