## Step 1: Define the symbolic representation of the variables
The variables are 'orange trees', 'basil plants', and 'apple trees', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ for 'orange trees'
- $x_1$ for 'basil plants'
- $x_2$ for 'apple trees'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $9x_0 + 8x_1 + 3x_2$.

## 4: Define the constraints in symbolic notation
The constraints are:
- $7x_0 + 17x_1 + 12x_2 \geq 38$ (total water need minimum 38 inches per week)
- $7x_0 + 12x_2 \leq 162$ (maximum water available for orange trees and apple trees)
- $7x_0 + 17x_1 \leq 78$ (total water need of orange trees and basil plants maximum 78 inches per week)
- $17x_1 + 12x_2 \leq 131$ (maximum water available for basil plants plus apple trees)
- $7x_0 + 17x_1 + 12x_2 \leq 131$ (total water need maximum 131 inches per week)
- $7x_0 + 16x_1 \leq 218$ (total combined beauty rating from orange trees and basil plants maximum 218)
- $16x_1 + 13x_2 \leq 205$ (total combined beauty rating from basil plants and apple trees maximum 205)
- $7x_0 + 16x_1 + 13x_2 \leq 205$ (total combined beauty rating from all maximum 205)

## 5: Specify the integer constraints
- $x_0$ is an integer (orange trees)
- $x_1$ is an integer (basil plants)
- $x_2$ is an integer (apple trees)

## 6: Create the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'orange trees'), ('x1', 'basil plants'), ('x2', 'apple trees')],
    'objective_function': '9*x0 + 8*x1 + 3*x2',
    'constraints': [
        '7*x0 + 17*x1 + 12*x2 >= 38',
        '7*x0 + 12*x2 <= 162',
        '7*x0 + 17*x1 <= 78',
        '17*x1 + 12*x2 <= 131',
        '7*x0 + 17*x1 + 12*x2 <= 131',
        '7*x0 + 16*x1 <= 218',
        '16*x1 + 13*x2 <= 205',
        '7*x0 + 16*x1 + 13*x2 <= 205'
    ]
}
```

## 7: 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
    x0 = model.addVar(name='orange_trees', vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name='basil_plants', vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name='apple_trees', vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(9 * x0 + 8 * x1 + 3 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(7 * x0 + 17 * x1 + 12 * x2 >= 38)
    model.addConstr(7 * x0 + 12 * x2 <= 162)
    model.addConstr(7 * x0 + 17 * x1 <= 78)
    model.addConstr(17 * x1 + 12 * x2 <= 131)
    model.addConstr(7 * x0 + 17 * x1 + 12 * x2 <= 131)
    model.addConstr(7 * x0 + 16 * x1 <= 218)
    model.addConstr(16 * x1 + 13 * x2 <= 205)
    model.addConstr(7 * x0 + 16 * x1 + 13 * x2 <= 205)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objVal)
        print('Orange Trees: ', x0.varValue)
        print('Basil Plants: ', x1.varValue)
        print('Apple Trees: ', x2.varValue)
    else:
        print('The model is infeasible')

solve_optimization_problem()
```