To solve the optimization problem described, we will use the Gurobi Python interface. The problem involves maximizing an objective function that includes terms related to the quantities of orange trees, zucchini vines, and agave, subject to several constraints related to their growth speeds and combined effects.

The objective function to maximize is:
\[8 \times (\text{orange trees})^2 + 8 \times (\text{orange trees}) \times (\text{zucchini vines}) + 8 \times (\text{orange trees}) \times (\text{agave}) + 2 \times (\text{zucchini vines})^2 + 7 \times (\text{zucchini vines}) \times (\text{agave}) + 3 \times (\text{agave})^2\]

The constraints are:
1. Growth speed of orange trees: \(23\)
2. Growth speed of zucchini vines: \(9\)
3. Growth speed of agave: \(17\)
4. Combined growth speed of orange trees and agave: \(\geq 61\)
5. Combined squared growth speeds of zucchini vines and agave: \(\geq 22\)
6. Total combined growth speed of all three: \(\geq 30\)
7. Maximum combined squared growth speeds of orange trees and agave: \(\leq 145\)
8. Maximum total combined growth speed of all three: \(\leq 145\)
9. Orange trees, zucchini vines, and agave must be whole numbers.

Let's denote:
- \(x_0\) as the number of orange trees,
- \(x_1\) as the number of zucchini vines,
- \(x_2\) as the number of agave.

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="orange_trees")
x1 = m.addVar(vtype=GRB.INTEGER, name="zucchini_vines")
x2 = m.addVar(vtype=GRB.INTEGER, name="agave")

# Objective function: Maximize
m.setObjective(8*x0**2 + 8*x0*x1 + 8*x0*x2 + 2*x1**2 + 7*x1*x2 + 3*x2**2, GRB.MAXIMIZE)

# Constraints
m.addConstr(x0 * 23 >= 61 - 17 * x2, name="orange_and_agave_min")
m.addConstr((x1 * 9)**2 + (x2 * 17)**2 >= 22, name="zucchini_and_agave_squared_min")
m.addConstr(23*x0 + 9*x1 + 17*x2 >= 30, name="total_growth_speed_min")
m.addConstr((x0 * 23)**2 + (x2 * 17)**2 <= 145, name="orange_and_agave_max")
m.addConstr(23*x0 + 9*x1 + 17*x2 <= 145, name="total_growth_speed_max")

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Orange Trees: {x0.x}")
    print(f"Zucchini Vines: {x1.x}")
    print(f"Agave: {x2.x}")
else:
    print("No optimal solution found")
```