To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's denote the number of acres of corn as `x1` and the number of acres of cabbage as `x2`.

The symbolic representation of the problem is:
```json
{
  'sym_variables': [('x1', 'acres of corn'), ('x2', 'acres of cabbage')],
  'objective_function': '50*x1 + 70*x2',
  'constraints': [
    'x1 + 1.5*x2 <= 200',  # Tractor time constraint
    '2*x1 + 3*x2 <= 275',  # Hand-picking time constraint
    'x1 + x2 <= 200',      # Total land constraint
    'x1 >= 0',             # Non-negativity constraint for corn
    'x2 >= 0'              # Non-negativity constraint for cabbage
  ]
}
```
Now, let's write the Gurobi code to solve this optimization problem:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="acres_of_corn")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="acres_of_cabbage")

# Set objective function
m.setObjective(50*x1 + 70*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + 1.5*x2 <= 200, "tractor_time_constraint")
m.addConstr(2*x1 + 3*x2 <= 275, "hand_picking_time_constraint")
m.addConstr(x1 + x2 <= 200, "total_land_constraint")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Acres of corn: {x1.x}")
    print(f"Acres of cabbage: {x2.x}")
    print(f"Maximum profit: ${50*x1.x + 70*x2.x:.2f}")
else:
    print("No optimal solution found")
```