To tackle this problem, let's first break down the given information and then translate it into a symbolic representation. We have two variables: 'agave' and 'orange trees', which we'll represent symbolically as `x0` and `x1`, respectively.

Given:
- The objective function to maximize is `2.08*x0 + 3.75*x1`.
- Constraints include:
  - Resilience index of agave (`13*x0`) plus resilience index of orange trees (`15*x1`) must be greater than or equal to 37 and less than or equal to 43.
  - Cost of agave (`9*x0`) plus cost of orange trees (`12*x1`) must be at least $42 and no more than $85.
  - The constraint `-4*x0 + 7*x1 >= 0`.
  - Both `x0` (agave) and `x1` (orange trees) must be whole numbers.

The symbolic representation of the problem is:
```json
{
  'sym_variables': [('x0', 'agave'), ('x1', 'orange trees')],
  'objective_function': '2.08*x0 + 3.75*x1',
  'constraints': [
    '13*x0 + 15*x1 >= 37',
    '13*x0 + 15*x1 <= 43',
    '9*x0 + 12*x1 >= 42',
    '9*x0 + 12*x1 <= 85',
    '-4*x0 + 7*x1 >= 0'
  ]
}
```

Now, let's write the Gurobi code to solve this optimization problem. We'll use Python as our programming language.

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(2.08*x0 + 3.75*x1, GRB.MAXIMIZE)

# Add constraints
m.addConstr(13*x0 + 15*x1 >= 37, "resilience_index_lower")
m.addConstr(13*x0 + 15*x1 <= 43, "resilience_index_upper")
m.addConstr(9*x0 + 12*x1 >= 42, "cost_lower")
m.addConstr(9*x0 + 12*x1 <= 85, "cost_upper")
m.addConstr(-4*x0 + 7*x1 >= 0, "additional_constraint")

# Optimize the model
m.optimize()

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