## Step 1: Define the symbolic representation of the variables
The variables are 'orange trees', 'zucchini vines', and 'agave', which we will represent symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $8x_1^2 + 8x_1x_2 + 8x_1x_3 + 2x_2^2 + 7x_2x_3 + 3x_3^2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
1. $23x_1 \geq 0$ (implicit, as $x_1$ is non-negative),
2. $9x_2 \geq 0$ (implicit, as $x_2$ is non-negative),
3. $17x_3 \geq 0$ (implicit, as $x_3$ is non-negative),
4. $23x_1 + 17x_3 \geq 61$,
5. $9^2x_2^2 + 17^2x_3^2 \geq 22$ or $81x_2^2 + 289x_3^2 \geq 22$,
6. $23x_1 + 9x_2 + 17x_3 \geq 30$,
7. $23^2x_1^2 + 17^2x_3^2 \leq 145$ or $529x_1^2 + 289x_3^2 \leq 145$,
8. $23x_1 + 9x_2 + 17x_3 \leq 145$,
9. $x_1$ is an integer,
10. $x_2$ is an integer,
11. $x_3$ is an integer.

## 4: Resource attributes
Given resources/attributes:
- $r_0$: {'description': 'growth speed', 'upper_bound': 183, 'x0': 23, 'x1': 9, 'x2': 17}

## 5: Symbolic Representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'orange trees'), ('x2', 'zucchini vines'), ('x3', 'agave')],
'objective_function': '8*x1^2 + 8*x1*x2 + 8*x1*x3 + 2*x2^2 + 7*x2*x3 + 3*x3^2',
'constraints': [
    '23*x1 + 17*x3 >= 61',
    '81*x2^2 + 289*x3^2 >= 22',
    '23*x1 + 9*x2 + 17*x3 >= 30',
    '529*x1^2 + 289*x3^2 <= 145',
    '23*x1 + 9*x2 + 17*x3 <= 145',
    'x1 >= 0 and x1 is an integer',
    'x2 >= 0 and x2 is an integer',
    'x3 >= 0 and x3 is an integer'
]
}
```

## 6: Gurobi Code
Now, let's implement this in Gurobi using Python:
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="orange_trees", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="zucchini_vines", vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name="agave", vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(8*x1**2 + 8*x1*x2 + 8*x1*x3 + 2*x2**2 + 7*x2*x3 + 3*x3**2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(23*x1 + 17*x3 >= 61)
    model.addConstr(81*x2**2 + 289*x3**2 >= 22)
    model.addConstr(23*x1 + 9*x2 + 17*x3 >= 30)
    model.addConstr(529*x1**2 + 289*x3**2 <= 145)
    model.addConstr(23*x1 + 9*x2 + 17*x3 <= 145)

    # Non-negativity constraints are implicitly handled by Gurobi for integer variables

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("Orange Trees: ", x1.varValue)
        print("Zucchini Vines: ", x2.varValue)
        print("Agave: ", x3.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```