## Step 1: Define the symbolic representation of the variables
The variables are 'agave' and 'orange trees'. Let's denote 'agave' as $x_1$ and 'orange trees' as $x_2$.

## Step 2: Define the attributes of the variables
The attributes given are:
- $r_0$: resilience index
- $r_1$: dollar cost
With specific values:
- For 'agave' ($x_1$): $r_0 = 13$, $r_1 = 9$
- For 'orange trees' ($x_2$): $r_0 = 15$, $r_1 = 12$

## 3: Formulate the objective function
The objective function to maximize is $2.08x_1 + 3.75x_2$.

## 4: Formulate the constraints
1. The resilience index of agave is 13: $13x_1 \geq 13x_1$ (always true, not a constraint)
2. agave are 9 dollars: $9x_1 \geq 9x_1$ (always true, not a constraint)
3. orange trees each have a resilience index of 15: $15x_2 \geq 15x_2$ (always true, not a constraint)
4. orange trees are each 12 dollars: $12x_2 \geq 12x_2$ (always true, not a constraint)
5. The total combined resilience index from agave plus orange trees must be equal to or greater than 37: $13x_1 + 15x_2 \geq 37$
6. You must spend at least 42 dollars on agave plus orange trees: $9x_1 + 12x_2 \geq 42$
7. $-4$ times the number of agave, plus seven times the number of orange trees should be greater than or equal to zero: $-4x_1 + 7x_2 \geq 0$
8. The total combined resilience index from agave plus orange trees should be equal to or less than 43: $13x_1 + 15x_2 \leq 43$
9. You have to spend no more than $85 on agave, and orange trees: $9x_1 + 12x_2 \leq 85$
10. You are restricted to a nonfractional number of agave: $x_1 \in \mathbb{Z}$
11. A whole number of orange trees must be used: $x_2 \in \mathbb{Z}$

## 5: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'agave'), ('x2', 'orange trees')],
    'objective_function': '2.08*x1 + 3.75*x2',
    'constraints': [
        '13*x1 + 15*x2 >= 37',
        '9*x1 + 12*x2 >= 42',
        '-4*x1 + 7*x2 >= 0',
        '13*x1 + 15*x2 <= 43',
        '9*x1 + 12*x2 <= 85',
        'x1 >= 0 and x1 in Integer', 
        'x2 >= 0 and x2 in Integer'
    ]
}
```

## 6: Gurobi Code
```python
import gurobi

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

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

    # Objective function
    model.setObjective(2.08*x1 + 3.75*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(13*x1 + 15*x2 >= 37)
    model.addConstr(9*x1 + 12*x2 >= 42)
    model.addConstr(-4*x1 + 7*x2 >= 0)
    model.addConstr(13*x1 + 15*x2 <= 43)
    model.addConstr(9*x1 + 12*x2 <= 85)

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Agave: {x1.varValue}")
        print(f"Orange Trees: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```