To solve this optimization problem, we first need to convert the natural language description into a symbolic representation. Let's define:

- $x_1$ as the number of egg sandwiches made,
- $x_2$ as the number of ham sandwiches made.

The objective function is to maximize profit. Given that each egg sandwich makes a profit of $3.5 and each ham sandwich makes a profit of $5, the objective function can be written as:

Maximize: $3.5x_1 + 5x_2$

Now, let's consider the constraints based on the available resources (eggs and ham):

- Each egg sandwich requires 5 eggs, and each ham sandwich requires 1 egg. The truck has a total of 50 eggs.
  - Constraint: $5x_1 + x_2 \leq 50$
- Each egg sandwich requires 2 slices of ham, and each ham sandwich requires 4 slices of ham. The truck has a total of 60 slices of ham.
  - Constraint: $2x_1 + 4x_2 \leq 60$

Additionally, we need to ensure that the number of sandwiches made is non-negative since you cannot make a negative number of sandwiches:

- $x_1 \geq 0$
- $x_2 \geq 0$

In symbolic representation, this problem can be described as:
```json
{
  'sym_variables': [('x1', 'number of egg sandwiches'), ('x2', 'number of ham sandwiches')],
  'objective_function': '3.5*x1 + 5*x2',
  'constraints': ['5*x1 + x2 <= 50', '2*x1 + 4*x2 <= 60', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's write the Gurobi code in Python to solve this optimization problem:
```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(3.5*x1 + 5*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(5*x1 + x2 <= 50, "eggs")
m.addConstr(2*x1 + 4*x2 <= 60, "ham")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
    print(f"Maximum profit: {m.objVal}")
else:
    print("No optimal solution found")
```