## Step 1: Define the symbolic representation of the variables
The variables are 'agave', 'chives', and 'boxwoods', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The objective function to maximize is $5x_0 + 3x_1 + 5x_2$.

## Step 3: List the constraints in symbolic notation
The constraints are:
- $3x_0 \geq 0$ (not explicitly stated but implied as growth speed is always positive)
- $5x_0 \geq 0$ (not explicitly stated but implied as water need is always positive)
- $4x_0 \geq 0$ (not explicitly stated but implied as cost is always positive)
- $5x_1 \geq 0$ (not explicitly stated but implied as growth speed is always positive)
- $x_1 \geq 0$ (not explicitly stated but implied as water need is always positive)
- $2x_1 \geq 0$ (not explicitly stated but implied as cost is always positive)
- $2x_2 \geq 0$ (not explicitly stated but implied as growth speed is always positive)
- $3x_2 \geq 0$ (not explicitly stated but implied as water need is always positive)
- $4x_2 \geq 0$ (not explicitly stated but implied as cost is always positive)
- $3x_0 + 5x_1 \geq 3$
- $3x_0 + 5x_1 + 2x_2 \geq 4$
- $5x_0 + x_1 \geq 24$
- $x_1 + 3x_2 \geq 20$
- $5x_0 + 3x_2 \geq 15$
- $5x_0 + x_1 + 3x_2 \geq 19$
- $2x_1 + 4x_2 \geq 13$
- $3x_0 + 5x_1 \leq 27$
- $5x_1 + 2x_2 \leq 10$
- $3x_0 + 5x_1 + 2x_2 \leq 20$
- $5x_0 + x_1 + 3x_2 \leq 47$ is redundant with $3x_0 + 5x_1 + 2x_2 \leq 20$ and other constraints
- $5x_0 + 3x_2 \leq 47$
- $5x_0 + x_1 \leq 70$
- $x_1 + 3x_2 \leq 95$
- $5x_0 + x_1 + 3x_2 \leq 47$
- $4x_0 + 4x_2 \leq 36$
- $2x_1 + 4x_2 \leq 27$
- $4x_0 + 2x_1 + 4x_2 \leq 27$

## 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'agave'), ('x1', 'chives'), ('x2', 'boxwoods')],
    'objective_function': '5*x0 + 3*x1 + 5*x2',
    'constraints': [
        '3*x0 + 5*x1 >= 3',
        '3*x0 + 5*x1 + 2*x2 >= 4',
        '5*x0 + x1 >= 24',
        'x1 + 3*x2 >= 20',
        '5*x0 + 3*x2 >= 15',
        '5*x0 + x1 + 3*x2 >= 19',
        '2*x1 + 4*x2 >= 13',
        '3*x0 + 5*x1 <= 27',
        '5*x1 + 2*x2 <= 10',
        '3*x0 + 5*x1 + 2*x2 <= 20',
        '5*x0 + 3*x2 <= 47',
        '5*x0 + x1 <= 70',
        'x1 + 3*x2 <= 95',
        '5*x0 + x1 + 3*x2 <= 47',
        '4*x0 + 4*x2 <= 36',
        '2*x1 + 4*x2 <= 27',
        '4*x0 + 2*x1 + 4*x2 <= 27'
    ]
}
```

## 5: Write the Gurobi code
```python
import gurobi

# Create a new model
m = gurobi.Model()

# Define the variables
x0 = m.addVar(name="agave", vtype=gurobi.GRB.INTEGER)
x1 = m.addVar(name="chives", vtype=gurobi.GRB.INTEGER)
x2 = m.addVar(name="boxwoods", vtype=gurobi.GRB.INTEGER)

# Define the objective function
m.setObjective(5*x0 + 3*x1 + 5*x2, gurobi.GRB.MAXIMIZE)

# Add constraints
m.addConstr(3*x0 + 5*x1 >= 3)
m.addConstr(3*x0 + 5*x1 + 2*x2 >= 4)
m.addConstr(5*x0 + x1 >= 24)
m.addConstr(x1 + 3*x2 >= 20)
m.addConstr(5*x0 + 3*x2 >= 15)
m.addConstr(5*x0 + x1 + 3*x2 >= 19)
m.addConstr(2*x1 + 4*x2 >= 13)
m.addConstr(3*x0 + 5*x1 <= 27)
m.addConstr(5*x1 + 2*x2 <= 10)
m.addConstr(3*x0 + 5*x1 + 2*x2 <= 20)
m.addConstr(5*x0 + 3*x2 <= 47)
m.addConstr(5*x0 + x1 <= 70)
m.addConstr(x1 + 3*x2 <= 95)
m.addConstr(5*x0 + x1 + 3*x2 <= 47)
m.addConstr(4*x0 + 4*x2 <= 36)
m.addConstr(2*x1 + 4*x2 <= 27)
m.addConstr(4*x0 + 2*x1 + 4*x2 <= 27)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Agave: ", x0.varValue)
    print("Chives: ", x1.varValue)
    print("Boxwoods: ", x2.varValue)
else:
    print("The model is infeasible")
```