## Step 1: Define the symbolic representation of the variables
The variables are 'apple trees', 'roses', 'peonies', 'coleus', 'chili plants' which can be represented symbolically as $x_1, x_2, x_3, x_4, x_5$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $1 \times (\text{apple trees}) + 4 \times (\text{roses}) + 3 \times (\text{peonies}) + 5 \times (\text{coleus}) + 4 \times (\text{chili plants})$. In symbolic terms, this is $x_1 + 4x_2 + 3x_3 + 5x_4 + 4x_5$.

## 3: Define the constraints in symbolic notation
1. $9x_1 + 7x_2 + 4x_3 + 9x_4 + 2x_5 \leq 231$ (total cost constraint)
2. $9x_1 + 2x_5 \geq 38$ (minimum spend on apple trees and chili plants)
3. $9x_1 + 7x_2 \geq 28$ (minimum spend on apple trees and roses)
4. $9x_1 + 9x_4 \geq 44$ (minimum spend on apple trees and coleus)
5. $4x_3 + 2x_5 \geq 45$ (minimum spend on peonies and chili plants)
6. $9x_1 + 4x_3 \geq 27$ (minimum spend on apple trees and peonies)
7. $7x_2 + 9x_4 \geq 39$ (minimum spend on roses and coleus)
8. $9x_1 + 7x_2 + 4x_3 + 9x_4 + 2x_5 \geq 39$ (total minimum spend)
9. $7x_2 + 4x_3 + 9x_4 \leq 87$ (maximum spend on roses, peonies, and coleus)
10. $4x_3 + 9x_4 + 2x_5 \leq 169$ (maximum spend on peonies, coleus, and chili plants)
11. $x_1 \in \mathbb{Z}$ (integer constraint on apple trees)
12. $x_2 \in \mathbb{Z}$ (integer constraint on roses)
13. $x_3 \in \mathbb{Z}$ (integer constraint on peonies)
14. $x_4 \in \mathbb{Z}$ (integer constraint on coleus)
15. $x_5 \in \mathbb{Z}$ (integer constraint on chili plants)

## 4: Create the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'apple trees'),
        ('x2', 'roses'),
        ('x3', 'peonies'),
        ('x4', 'coleus'),
        ('x5', 'chili plants')
    ],
    'objective_function': 'x1 + 4*x2 + 3*x3 + 5*x4 + 4*x5',
    'constraints': [
        '9*x1 + 7*x2 + 4*x3 + 9*x4 + 2*x5 <= 231',
        '9*x1 + 2*x5 >= 38',
        '9*x1 + 7*x2 >= 28',
        '9*x1 + 9*x4 >= 44',
        '4*x3 + 2*x5 >= 45',
        '9*x1 + 4*x3 >= 27',
        '7*x2 + 9*x4 >= 39',
        '9*x1 + 7*x2 + 4*x3 + 9*x4 + 2*x5 >= 39',
        '7*x2 + 4*x3 + 9*x4 <= 87',
        '4*x3 + 9*x4 + 2*x5 <= 169'
    ]
}
```

## 5: Write the Gurobi code to solve 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='apple_trees', vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name='roses', vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name='peonies', vtype=gurobi.GRB.INTEGER)
    x4 = model.addVar(name='coleus', vtype=gurobi.GRB.INTEGER)
    x5 = model.addVar(name='chili_plants', vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(x1 + 4*x2 + 3*x3 + 5*x4 + 4*x5, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(9*x1 + 7*x2 + 4*x3 + 9*x4 + 2*x5 <= 231)
    model.addConstr(9*x1 + 2*x5 >= 38)
    model.addConstr(9*x1 + 7*x2 >= 28)
    model.addConstr(9*x1 + 9*x4 >= 44)
    model.addConstr(4*x3 + 2*x5 >= 45)
    model.addConstr(9*x1 + 4*x3 >= 27)
    model.addConstr(7*x2 + 9*x4 >= 39)
    model.addConstr(9*x1 + 7*x2 + 4*x3 + 9*x4 + 2*x5 >= 39)
    model.addConstr(7*x2 + 4*x3 + 9*x4 <= 87)
    model.addConstr(4*x3 + 9*x4 + 2*x5 <= 169)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objval)
        print('Apple trees: ', x1.varValue)
        print('Roses: ', x2.varValue)
        print('Peonies: ', x3.varValue)
        print('Coleus: ', x4.varValue)
        print('Chili plants: ', x5.varValue)
    else:
        print('No optimal solution found')

solve_optimization_problem()
```