To solve this optimization problem, we need to define the decision variables, the objective function, and the constraints. 

The decision variables are:
- `agave`: The number of agave.
- `orange_trees`: The number of orange trees.

The objective function is:
- Maximize `2.08 * agave + 3.75 * orange_trees`.

The constraints are:
- Resilience index constraint: `13 * agave + 15 * orange_trees >= 37`.
- Dollar cost constraint: `9 * agave + 12 * orange_trees >= 42` and `9 * agave + 12 * orange_trees <= 85`.
- Additional constraints: `-4 * agave + 7 * orange_trees >= 0`, `13 * agave + 15 * orange_trees <= 43`.
- Integer constraints: Both `agave` and `orange_trees` must be integers.

Here is the Gurobi code that captures this optimization problem:
```python
from gurobipy import *

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

# Define the decision variables
agave = m.addVar(vtype=GRB.INTEGER, name="agave")
orange_trees = m.addVar(vtype=GRB.INTEGER, name="orange_trees")

# Set the objective function
m.setObjective(2.08 * agave + 3.75 * orange_trees, GRB.MAXIMIZE)

# Add constraints
m.addConstr(13 * agave + 15 * orange_trees >= 37, "resilience_index_constraint")
m.addConstr(-4 * agave + 7 * orange_trees >= 0, "additional_constraint")
m.addConstr(9 * agave + 12 * orange_trees >= 42, "dollar_cost_lower_bound")
m.addConstr(9 * agave + 12 * orange_trees <= 85, "dollar_cost_upper_bound")
m.addConstr(13 * agave + 15 * orange_trees <= 43, "resilience_index_upper_bound")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Agave:", agave.x)
    print("Orange Trees:", orange_trees.x)
    print("Objective function value:", m.objVal)
else:
    print("No optimal solution found.")
```