## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation.

Let's define the symbolic variables as follows:
- $x_1$ represents the acres of oak trees
- $x_2$ represents the acres of elm trees

The objective is to maximize profit, given that the profit per acre of oak trees is $1000 and the profit per acre of elm trees is $1200. Therefore, the objective function can be represented as:

Maximize: $1000x_1 + 1200x_2$

The constraints based on the problem description are:
1. Total acres of land: $x_1 + x_2 \leq 300$
2. Minimum acres of oak trees: $x_1 \geq 50$
3. Minimum acres of elm trees: $x_2 \geq 70$
4. Ratio of elm to oak trees: $x_2 \leq 2x_1$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'acres of oak trees'), ('x2', 'acres of elm trees')],
    'objective_function': '1000*x1 + 1200*x2',
    'constraints': [
        'x1 + x2 <= 300',
        'x1 >= 50',
        'x2 >= 70',
        'x2 <= 2*x1'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Tree_Optimization")

# Define the variables
x1 = model.addVar(name="oak_trees", lb=0, ub=300, vtype=gp.GRB.CONTINUOUS)
x2 = model.addVar(name="elm_trees", lb=0, ub=300, vtype=gp.GRB.CONTINUOUS)

# Objective function: Maximize profit
model.setObjective(1000*x1 + 1200*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 + x2 <= 300, name="total_acres")
model.addConstr(x1 >= 50, name="min_oak_trees")
model.addConstr(x2 >= 70, name="min_elm_trees")
model.addConstr(x2 <= 2*x1, name="elm_to_oak_ratio")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal acres of oak trees: {x1.varValue}")
    print(f"Optimal acres of elm trees: {x2.varValue}")
    print(f"Maximal profit: {model.objVal}")
else:
    print("The model is infeasible.")
```