To solve the given optimization problem, we first need to define the variables and the objective function symbolically. Let's denote the amount of 'aloe vera' as \(x_1\) and the number of 'cherry trees' as \(x_2\).

The symbolic representation of the problem is as follows:

```json
{
  "sym_variables": [("x1", "aloe vera"), ("x2", "cherry trees")],
  "objective_function": "6*x1 + 7*x2",
  "constraints": [
    "x1 >= 0", 
    "x2 >= 0", 
    "x1 + 15*x2 >= 39", 
    "7*x1 + 16*x2 >= 14", 
    "9*x1 + 12*x2 >= 38", 
    "14*x1 + 18*x2 >= 26", 
    "-4*x1 + 7*x2 >= 0", 
    "x1 + 15*x2 <= 72", 
    "7*x1 + 16*x2 <= 55", 
    "9*x1 + 12*x2 <= 99", 
    "14*x1 + 18*x2 <= 57",
    "x1 == int(x1)", 
    "x2 == int(x2)"
  ]
}
```

Given this symbolic representation, we can now formulate the problem in Gurobi. Note that Gurobi is a powerful tool for solving linear and mixed-integer programming problems but requires careful handling of integer constraints.

To model the problem in Gurobi, we need to import the necessary library, define our variables, objective function, and constraints according to the symbolic representation provided above.

Here's how you can do it:

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(vtype=GRB.INTEGER, name="aloe_vera")
x2 = m.addVar(vtype=GRB.INTEGER, name="cherry_trees")

# Define the objective function
m.setObjective(6*x1 + 7*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 >= 0, "non-negativity_aloe")
m.addConstr(x2 >= 0, "non-negativity_cherry")
m.addConstr(x1 + 15*x2 >= 39, "min_cost")
m.addConstr(7*x1 + 16*x2 >= 14, "min_water")
m.addConstr(9*x1 + 12*x2 >= 38, "min_yield")
m.addConstr(14*x1 + 18*x2 >= 26, "min_resilience")
m.addConstr(-4*x1 + 7*x2 >= 0, "custom_constraint")
m.addConstr(x1 + 15*x2 <= 72, "max_cost")
m.addConstr(7*x1 + 16*x2 <= 55, "max_water")
m.addConstr(9*x1 + 12*x2 <= 99, "max_yield")
m.addConstr(14*x1 + 18*x2 <= 57, "max_resilience")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Aloe Vera: {x1.x}")
    print(f"Cherry Trees: {x2.x}")
    print(f"Objective Value: {m.objVal}")
else:
    print("No optimal solution found")
```